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INTRACRANIAL HEMATOMA DETECTION
ii
INTRACRANIAL HEMATOMA DETECTION
Undergraduate graduation project Report
submitted in partial fulfillment of
the requirements for the
Degree of Bachelor of Science of Engineering
in
Department of Electronic & Telecommunication Engineering
University of Moratuwa
Supervisor
Dr. A. A. Pasqual
Project Group
G.K.I. Abayarathna 050001A
G. Gartheeban 050131V
E.D.R. Kumara 050234N
W.M.D. Soysa 050440R
May 19, 2009
Approval of the Department of Electronic and Telecommunication Engineering
Head, Department Of Electronic and
Telecommunication Engineering
This is to certify that we have read this project and that in our opinion it is fully
adequate, in scope and quality, as an Undergraduate Graduation Project.
Supervisor:
Name and Signature
Date:
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Abstract
THESIS TITLE
Supervisor: Dr. A. A. Pasqual
Keywords : intracranial hematoma detection, near infra-red, near infra-red spec-troscopy, iHD
Intracranial hematoma is a treatable potentially fatal secondary injury, with 80percentage of survival rate if identified and treated timely. Traumatic brain injury,the most prevailing cause of fatality in an accident, is one of the prime causes ofintracranial hematoma, along with post-surgery complications.
The nature of the causes requires portability, ability to mass produce, afford-ability in detection methods. However conventional detection technologies suchas CT scan and MRI, albeit being accurate and comprehensive, do not offer theaforementioned features. A portable, affordable, safety abiding device to detectintracranial hematoma has rich set of applications such as quick diagnosis, es-pecially on patients with no external wounds, wider availability, by making thedevices available in virtually every infirmaries, pre-scanning before enlistment forCT scans, on-site detection, especially on battle fields, nationwide catastrophes,etc.
This report describes the design and development of the intracranial hematomadetector (iHD) that detects the presence of the hematoma with above reasonableaccuracy. The solution consists of the iHD terminal and iHD mobile application(iHDMA). iHD terminal consists of Near Infra-red (NIR) LED and sensor systemto measure the absorption of NIR light. The measured values are encoded andtransmitted, through BluetoothTM , to a mobile computer running iHDMA . IniHDMA, using, optical density (OD) calculations, threshold detection, post detec-tion integration and bidirectional associative memory, the presence of hematomais ascertained.
In addition to reporting OD and probability estimation, iHD system can detectthe hematomas, semiautomatically for a given constant probability of false alarm.The system is currently accurate enough to detect Extracerebral Hematoma (EH)that could cause and absorbtion difference above 0.2 in optical density; it the samecategory that needs immediate attention as well.
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“To our parents, teachers and friends.”
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ACKNOWLEDGEMENTS
Dr. Ajith Pasqual has been a supportive and encouraging project advisor. He
showed a strong commitment and was very enthusiastic in extending a helping
hand. He was very approachable and open, on many occasions we were able to
contact him even in non-office hours, and notably one time, when he was on a
tour. Dr. Pasqual supervised two projects this year, and amidst his involvements
in numerous activities, he has always been there for us. In the middle, even when
we were about to follow through another project with another supervisor, he was
more than helpful. We thank him for being an excellent mentor and a guide.
Prof. J.A.K.S. Jayasinghe has been helpful is obtaining the enclosure within short
span of time. He, albeit his heavy schedule allocated time to check the design and
supervise the operation of the Rapid Prototyping Machine. We thank him for his
assistance.
Mr. Kithsiri Samarasinghe, former Head of the Department, offered valuable ad-
vices as a supervisor, mentor, lecturer, and Head of the Department. His popular
”five minute introduce yourself”, triggered many of us to reflect on ourselves and
encouraged us to be forward and assertive. More importantly his advices on final
year project procedures, engineering aspects and industry oriented preparation
were invaluable. We thank him for being a wonderful teacher.
Dr. Chulantha Kulasekare, Head of the Department, has served in the super-
visory panel during feasibility study presentation and offered valuable advices.
Further, during the project selection period, when we approached him with our
idea, he was very supportive and encouraging. He was clairvoyant in recognizing
the potential pitfalls and readily warned us about the caveats. We thank him for
being a supportive educator.
We are in debt to Dr. Kosala Ranatunga for proposing the idea, arranging meet-
ings with the Director of the Accident ward, General Hospital, Colombo and other
surgeons, and taking all the trouble in obtaining permissions. He was very sup-
portive during the last phase of the project and visited on the 17th of May to
vi
inspect the operation of the device as well. We thank him for his facilitation.
We also thank Dr. Himashi Kularathne, neurosurgeon, Head of Neural Surgi-
cal Ward, General Hospital, Colombo for granting us permission to conduct field
tests on patients undergoing surgery for extracerebral hematoma.
Mr. Udaya Chinthaka Jayatilake, lecturer, Department of mathematics, is a pro-
ficient mathematician who is keen on exploring the impossibles. He introduced
us Artificial Neural Networks and Bidirectional Associative Memory. His lectures
were simple and informative, always looking for ways to integrate with practical
applications. We thank him for being a guide to elusive mathematics.
A number of people have helped us in iHD project. Sashitha Nalin, from De-
partment of Mechanical engineering, was helpful in designing the chassis. Few
other batch mates offered their valuable advice throughout the project. We also
thank the staff from Engineering Design Center for letting us use Rapid Prototype
machine and their help in using it. We also thank other staff members, technical
assistants, and non-academic staff for all their support.
We also thank our parents and friends for being there, during our tenure at Uni-
versity of Moratuwa, supporting us financially and otherwise, and tolerating us
during the hard times, especially during the busy days.
vii
TABLE OF CONTENTS
APPROVAL iii
ABSTRACT iv
DEDICATIONS v
ACKNOWLEDGEMENTS vi
TABLE OF CONTENTS viii
LIST OF FIGURES xii
LIST OF TABLES xiv
ABBREVIATIONS xv
1 INTRODUCTION 1
1.1 Intracranial Hematoma Detector (iHD) . . . . . . . . . . . . . . . . 3
1.2 Organization of the Report . . . . . . . . . . . . . . . . . . . . . . . 5
2 LITERATURE SURVEY 6
2.1 Intracranial Hematoma (IH) . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 Types of Intracranial Hematoma . . . . . . . . . . . . . . . 7
2.1.2 Traumatic Brain Injury (TBI) . . . . . . . . . . . . . . . . . 9
2.2 Other diagnosis procedures . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Computed Axial Tomography (CAT) . . . . . . . . . . . . . 10
2.2.2 Magnetic Resonance Imaging (MRI) . . . . . . . . . . . . . 10
2.3 Near Infrared Spectroscopy (NIRS) . . . . . . . . . . . . . . . . . . 11
2.3.1 Application of near infrared spectroscopy for intracranialhematoma detection . . . . . . . . . . . . . . . . . . . . . . 12
2.4 Safety Regulations . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5.1 Threshold detection . . . . . . . . . . . . . . . . . . . . . . . 15
2.5.2 Post detection integration . . . . . . . . . . . . . . . . . . . 15
2.5.3 Bidirectional Associative Memory (BAM) . . . . . . . . . . 16
2.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
viii
3 SYSTEM OPERATION 17
3.1 Using the system . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2 Operation of iHD terminal . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Operation of iHDMA . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4 SYSTEM ARCHITECTURE 26
4.1 iHD System Architecture . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2 iHD Hardware Architecture . . . . . . . . . . . . . . . . . . . . . . 27
4.2.1 Signal generation and Sensor Subsystem . . . . . . . . . . . 28
4.2.1.1 Pulse generation . . . . . . . . . . . . . . . . . . . 29
4.2.1.2 NIR light emission . . . . . . . . . . . . . . . . . . 29
4.2.1.3 Signal reception, amplification and Analog to Dig-ital Conversion (ADC) . . . . . . . . . . . . . . . . 31
4.2.1.4 Interfacing and Transmitter - Receiver Separation . 33
4.2.2 Processing & Controlling Subsystem . . . . . . . . . . . . . 36
4.2.2.1 PIC Microcontroller . . . . . . . . . . . . . . . . . 36
4.2.2.2 Sensor Interfacing . . . . . . . . . . . . . . . . . . 37
4.2.2.3 Possible methods of computation . . . . . . . . . . 38
4.2.3 Communication Subsystem . . . . . . . . . . . . . . . . . . . 39
4.2.3.1 UART . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2.4 Power Management Subsystem . . . . . . . . . . . . . . . . 42
4.2.4.1 Low Dropout (LDO) Regulators . . . . . . . . . . 42
4.2.4.2 Power Source . . . . . . . . . . . . . . . . . . . . . 43
4.2.4.3 DC Power and Charging . . . . . . . . . . . . . . . 43
4.2.4.4 Sleep, Power Saving Modes . . . . . . . . . . . . . 44
4.2.5 Input Output Subsystem . . . . . . . . . . . . . . . . . . . . 44
4.2.6 Safety concerns . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 iHD Firmware Architecture . . . . . . . . . . . . . . . . . . . . . . 45
4.3.1 Communication state implementation . . . . . . . . . . . . . 46
4.3.2 Data Acquisition state implementation . . . . . . . . . . . . 46
4.3.3 User Indication IO . . . . . . . . . . . . . . . . . . . . . . . 46
4.4 Communication Protocol . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4.2 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.4.3 Message Starting Header . . . . . . . . . . . . . . . . . . . . 48
4.4.4 Message Body . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4.5 Message-ending Header . . . . . . . . . . . . . . . . . . . . . 49
4.4.6 Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5 HEMATOMA LIKELIHOOD ESTIMATION IN iHDMA 50
ix
5.1 Optical Density calculation on Isolated Scans . . . . . . . . . . . . 50
5.1.1 Merits and Demerits . . . . . . . . . . . . . . . . . . . . . . 51
5.2 Characteristics of Intensity values and OD . . . . . . . . . . . . . . 51
5.3 Reference Estimation through normalization . . . . . . . . . . . . . 52
5.3.1 Moving average based normalization . . . . . . . . . . . . . 52
5.4 Threshold detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.5 Post detection integration . . . . . . . . . . . . . . . . . . . . . . . 57
5.6 Application of Bidirectional Associative Memory (BAM) . . . . . . 58
5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6 RESULTS, EVALUATION AND DISCUSSION 60
6.1 Testing procedures and clinical trials . . . . . . . . . . . . . . . . . 60
6.1.1 Trial Objectives and Purpose . . . . . . . . . . . . . . . . . 60
6.1.2 Trial Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
6.1.3 Selection and Withdrawal of Subjects . . . . . . . . . . . . . 61
6.1.4 Treatment of Subjects . . . . . . . . . . . . . . . . . . . . . 61
6.1.5 Assessment of Efficacy . . . . . . . . . . . . . . . . . . . . . 62
6.1.6 Assessment of Safety . . . . . . . . . . . . . . . . . . . . . . 62
6.1.7 Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.2 Evaluation and Discussion . . . . . . . . . . . . . . . . . . . . . . . 63
6.2.1 Sensor outputs . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.2.2 Performance metrics . . . . . . . . . . . . . . . . . . . . . . 63
6.3 Presentation of results . . . . . . . . . . . . . . . . . . . . . . . . . 64
7 CONCLUSION 69
7.1 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
7.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
7.3 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
BIBLIOGRAPHY 72
APPENDICES 77
A iHD SCHEMATIC 77
B COMMUNICATION PROTOCOL SPECIFICATION 79
B.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
B.1.1 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
B.2 Message-starting Header . . . . . . . . . . . . . . . . . . . . . . . . 81
B.3 Message Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
B.4 Message-ending Header . . . . . . . . . . . . . . . . . . . . . . . . . 82
B.5 Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
B.6 Example Word Patterns . . . . . . . . . . . . . . . . . . . . . . . . 83
x
B.6.1 Word Types . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
B.6.2 Message ID . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
B.6.3 Command or Reply . . . . . . . . . . . . . . . . . . . . . . . 84
B.6.4 Frame ID . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
B.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
C DATASHEETS 86
D BIDIRECTIONAL ASSOCIATIVE MEMORY (BAM) 91
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LIST OF FIGURES
1.1 Intracranial Hematoma detector . . . . . . . . . . . . . . . . . . . . 4
1.2 Banana-shaped light path . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 Traumatic Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 NIR Absorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Optical Density Histogram . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 Significance of Optical Density variations . . . . . . . . . . . . . . . 14
3.1 Modes of operations of iHD terminal . . . . . . . . . . . . . . . . . 18
3.2 Optical Density Scan Positions . . . . . . . . . . . . . . . . . . . . . 19
3.3 Visualizing Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.4 Complete Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.5 iHDMA capture operation . . . . . . . . . . . . . . . . . . . . . . . 22
3.6 iHDMA store and data-process operation . . . . . . . . . . . . . . . 23
3.7 iHDMA advanced analysis operation . . . . . . . . . . . . . . . . . 24
3.8 iHDMA configurations . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1 iHD System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 iHD Hardware Architecture . . . . . . . . . . . . . . . . . . . . . . 28
4.3 Burst of Pulses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.4 OPT101 Sesor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.5 OPT101 High sensitive light to voltage converter . . . . . . . . . . 32
4.6 Dark Current Offset Correction . . . . . . . . . . . . . . . . . . . . 33
4.7 NIR LED and Sensor attachment . . . . . . . . . . . . . . . . . . . 34
4.8 Flexible Elastic Optical Probe . . . . . . . . . . . . . . . . . . . . . 35
4.9 Bluetooth Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.10 Bluetooth Integration via UART . . . . . . . . . . . . . . . . . . . 41
4.11 Bluetooth Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.12 Firmware Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.1 Intensity Samples (Before Normalization) . . . . . . . . . . . . . . . 53
5.2 Intensity Samples (After Normalization) . . . . . . . . . . . . . . . 53
5.3 Dynamic threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.4 Results after threshold detection . . . . . . . . . . . . . . . . . . . . 56
5.5 Post detection integration . . . . . . . . . . . . . . . . . . . . . . . 57
6.1 Reference samples before normalization . . . . . . . . . . . . . . . . 64
6.2 Reference samples with normalization factor 4 . . . . . . . . . . . . 65
xii
6.3 Reference samples with normalization factor 9 . . . . . . . . . . . . 65
6.4 Reference samples with triangular normalization function . . . . . . 66
6.5 Reference samples with triangular normalization function . . . . . . 66
6.6 OD calculation on scan and reference intensities . . . . . . . . . . . 67
6.7 BAM to filter out unlikely cases . . . . . . . . . . . . . . . . . . . . 67
6.8 Visualization of the results. . . . . . . . . . . . . . . . . . . . . . . 68
6.9 Presentation of Quick scan OD output . . . . . . . . . . . . . . . . 68
A.1 iHD SCHEMATIC 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 77
A.2 iHD SCHEMATIC 2 . . . . . . . . . . . . . . . . . . . . . . . . . . 78
B.1 Typical Message . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
B.2 Message-starting Header . . . . . . . . . . . . . . . . . . . . . . . . 81
B.3 Types of Message Starting Headers (Commands) . . . . . . . . . . . 81
B.4 Types of Message Starting Headers (Replies) . . . . . . . . . . . . . 81
B.5 Message Body of Ping Reply . . . . . . . . . . . . . . . . . . . . . . 82
B.6 Message Body of Update settings and request settings reply . . . . 82
B.7 Message Body of Request Data . . . . . . . . . . . . . . . . . . . . 82
B.8 Message-ending Header . . . . . . . . . . . . . . . . . . . . . . . . . 82
B.9 Frame-idle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
B.10 Data-Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
B.11 Typical word . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
C.1 Datasheet - PIC 18F452 . . . . . . . . . . . . . . . . . . . . . . . . 86
C.2 Pin Diagram - PIC 18F452 . . . . . . . . . . . . . . . . . . . . . . . 87
C.3 Specifications of OPT101 . . . . . . . . . . . . . . . . . . . . . . . . 88
C.4 OPAMP characteristics of OPT101 . . . . . . . . . . . . . . . . . . 89
C.5 Datasheet - NIR LED . . . . . . . . . . . . . . . . . . . . . . . . . 90
xiii
LIST OF TABLES
4.1 iHD terminal power budget . . . . . . . . . . . . . . . . . . . . . . 42
4.2 Message Starting Header . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3 Types of message commands . . . . . . . . . . . . . . . . . . . . . . 48
xiv
ABBREVIATIONS
ADC Analog - to - Digital Converter
ANN Artificial Neural Network
APD Avalanche Photodiodes
BAM Bidirectional Associative Memory
BT Bluetooth
CAT Computed Axial Tomography
CCD Charge Coupled Devices
CT Computer Tomography
EEPROM Electrically Erasable Programmable Read-Only Memory
EH Extracerebral Hematoma
FDA Food and Drug Administration
IH Intracranial Hematoma
iHD intracranial Hematoma Detector
iHDMA intracranial Hematoma Detector Mobile Application
LCD Liquid Crystal Display
LED Light Emitting Diode
MRI Magnetic Resonance Imaging
NIR Near Infra-Red
NIRS Near Infra-Red Spectroscopy
OT Optical Topography
PDA Personal Digital Assistant
PMT PhotoMultiplier Tubes
PWM Pulse Width Modulation
SiPD Silicon Photodiodes
SPP Serial Port Profile
TBI Traumatic Brain Injury
xv
Chapter 1
INTRODUCTION
Medical diagnosis is the process of determining the presence of a disease in an
individual, followed by the confirmation through analysis and past experience of
similar observations [1]. Technology has enabled us to utilize elaborate equipments
to diagnose with greater accuracy and precision, in addition to conventional physi-
cal examinations, with the assistance of Medical Technologists [2]. Nonetheless our
dynamic lifestyles, exorbitant price of medical equipments, exiguity of advanced
equipments in developing countries and remote areas, and lack of technical exper-
tise have always been in favor of portable simple on-site detection technologies.
Intracranial hematoma detection is a medical diagnosis of determining the pres-
ence of hemorrhage inside the cranium, which could potentially lead to death if
not detected and treated timely.
While advanced high-priced detection methods like Computed Tomography (CT)
[3] is widespread today, we are motivated by the promise of affordable portable
technologies which could benefit people living in developing countries like Sri
Lanka. In addition, a portable device is highly desirable in on-site detection and
thus unveil another set of applications. A potential list of such applications are,
• Quick diagnosis : Patients with no external wounds are likely to be over-
looked and hence might result in inadvertent severity. In addition, patients
with inconspicuous and latent symptoms might also suffer the same fate.
Further, due to overlapping symptoms it is increasingly becoming common
1
to be misdiagnosed when sophisticated tests are not carried out [4]. In devel-
oping countries like Sri Lanka where the medical tests are costly and equip-
ments are scanty, this is not a rare case. Wider availability of an affordable,
less cumbersome device to provide positive confirmation of the disease will
be of great value to a physician in making a well informed decision (Courtesy
- Dr. E. C. Kulasekare)
• Wider availability: By making Intracranial Hematoma Detector (iHD)
available in virtually every infirmary, a quick diagnosis can be performed
and on positive indication they could be referred to teaching hospitals with
adequate facilities for additional tests and further treatment.
• Pre-scanning before enlisting to CT scans : CT equipments are pro-
hibitively expensive and hardly affordable by a standard hospital in devel-
oping countries such as Sri Lanka. This leads to a long waiting list for the
usage that results in unacceptable delays and deaths that could have been
otherwise prevented. Such a device could be used as a preliminary measure
to filter out the patients by confirming the presence of hematoma.
• On-site detection : During a public accident or nationwide catastrophe
not everyone needs the same medical attention. While people with benign ex-
ternal wounds could wait post first-aid treatment, those who are with urgent
medical conditions should be transferred and attended immediately. How-
ever in the absence of an external wound, Traumatic Brain Injuries (TBI), a
major cause of death and disability worldwide [5], are not detected and hence
lead to fatalities. This is common in battle fields, accidents in sports and
adventure and industrial mishaps. An on-site detection technology addresses
the particular issue at hand.
Observing the distinctions between traditional equipments and portable devices,
it becomes clear that they are in fact complementing each other rather than sub-
stituting, thus improves the outcome together. Due to the fundamental difference
2
between the underlying technology, otherwise-would-have-been-trivial factors such
as usability, noise treatment, power consumption, etc become significant. There-
fore, unusual measures have to be undertaken. In determining the successfulness
of solution, the following are deemed to be critical:
• Safety assessment: Since it is a medical instrument that directly deals
with patients it should abide by the safety regulations and not become the
cause for further complications. Primary concerns are heat generation and
exposure to radiation.
• Reliable: The results should reflect the actual condition with high proba-
bility and device must be capable of self learning to adjust its parameter to
improve the accuracy in semi-supervised manner.
• Affordable: To be able to ensure the wider availability, especially to allow
penetration into developing countries, the cost of a single device must be
affordable without compromising the quality and features.
• Portable: One of the main application is on-site detection, to make it
possible the solution need to be a battery powered handheld device. For ad-
vanced processing and computations, a mobile computer will be used which
will essentially bring the cost of the device down.
• Power consumption : As it is battery powered, maximum power dissi-
pation is limited.
1.1 Intracranial Hematoma Detector (iHD)
This report describes the design, implementation, and evaluation of iHD system
(figure: 1.1). It consists of hardware device - intracranial hematoma detector ter-
minal (iHD terminal), firmware running in the terminal and software application -
intracranial hematoma detector mobile application (iHDMA). The device operates
3
Figure 1.1: iHD with its top enclosure removed.
in several modes and the fundamental operation is to transmit a burst of pulsed
energy of Near-Infrared (NIR) light, let it take a banana shaped path [6] along the
cranium (figure: 1.2), and measure the intensity on its reception.
Figure 1.2: The assumed banana-shaped light path through tissue sample.
Analog to digital converted value of intensity corresponds to the absorption of
NIR light along its path. Using conventional Optical Density (OD) calculations
presence of hematoma can be directly calculated [7]. However, to reduce the
4
complexity and improve the feature set, we propose few enhancements that are
based on radar system principles.
Instead of single scan, continuous scans will be carried out to generate several sam-
ples that will be put through various methods and pattern matching algorithms to
semi-automatically detect the presence of hematoma, and provide visual imaging
of the head.
1.2 Organization of the Report
The remainder of this report discusses the background, design and development
of iHD system and evaluation of the system in real world application. Chapter
2 introduces to the general background and related work. Chapter 3 discusses
operation of the terminal and mobile application. We present the implementation
of the hardware and firmware of iHD system in Chapter 4. Chapter 5 describes the
algorithms used for hematoma likelihood estimation in iHDMA. Chapter 6 presents
the evaluation of the solution developed and discusses the results. Finally, Chapter
7 summarizes our work and discusses the possible future directions.
5
Chapter 2
LITERATURE SURVEY
Intracranial hematoma is one of the profoundly studied injuries in medical surg-
eries. Intracranial hematoma is a treatable cause of secondary injury which can
cause significant disability or death if not promptly recognized and treated. They
occur as the primary injury in 40% of patients with severe head injury. Recurrent
hematomas, postoperative epidural hematomas, and delayed traumatic intracere-
bral hematomas develop in up to 23% of patients with severe head injury [7].
Near infrared spectroscopy (NIRS) is a spectroscopic method utilizing the near
infrared region of the electromagnetic spectrum (from 700 nm to 1400 nm). NIRS
can be used for non-invasive assessment of the brain function through an intact
skull in human subjects by detecting changes in blood hemoglobin concentrations.
This application is sometimes called optical topography (OT) in which NIRS is
used for functional mapping of the human cortex.
This chapter presents the background and discusses the related work in the area
of intracranial hematoma detection. Section 2.1 begins with a general overview
of intracranial hematoma and discusses the causes and implications. Section 2.2
lists the current diagnosis procedures, merits and demerits. Section 2.3 gives an
overview of NIRS, examines its applicability for intracranial hematoma detection
and surveys related work in this area. Section 2.4 discusses the safety regulations.
Section 2.5 examines different techniques to estimate the presence of hematoma
from intensity data.
6
2.1 Intracranial Hematoma (IH)
An intracranial hematoma is a hemorrhage that occurs within the skull. Intracra-
nial bleeding occurs when a blood vessel within the skull ruptures or leaks. It can
result from physical trauma or non-traumatic causes [8].
Intracranial hematoma possesses a serious medical emergency because the accumu-
lation of blood within the skull can lead to increase in intracranial pressure, which
can crush delicate brain tissues or limit its blood supply thus cause a secondary
injury. Pressure increase can be lethal under certain circumstance where it could
leave a potentially deadly brain herniation. Early identification prior to neuro-
logical deterioration, is the key to successful surgical treatment. This is currently
accomplished by serial CT scan because it is the only reliable method currently
available.
2.1.1 Types of Intracranial Hematoma
• Intra-axial Hematoma
• Extra-axial Hematoma
– Subgaleal Hematoma: Hematoma that occurs between the galea aponeu-
rosis and skull periosteum.
– Cephalhematoma: Hematoma that occurs between the skull periosteum
and skull.
– Epidural Hematoma: Hematoma that occurs between the skull and
dura mater. The condition is potentially deadly because the buildup
of blood may increase pressure in the intracranial space and compress
delicate brain tissue. More often, a tear in the middle meningeal artery
causes this type of hematoma. When hematoma occurs from laceration
7
of an artery, blood collection can cause rapid neurologic deterioration.
Although it occurs in 1-3 % of head injuries between 15 and 20% of
patients with epidural hematomas die of the injury [9] [10].
– Subdural hematoma: Hematoma that occurs between the dura mater
and arachnoid mater. They often occur as a primary head injury due to
fast changing velocities within skull, that leave tears in small bridging
veins. Much more common injuries happen due to various rotational or
linear forces. Further, it is more common in patients on anticoagulants,
such as aspirin and warfarin, and can have a subdural hematoma with a
minor injury. The associated mortality rate is high, approximately 60-
80%. Traditional methods like CT scan or MRI scan are commonly used
to detect subdural hematomas [11]. While small subdural hematomas
can be managed by careful monitoring until the body heals itself, larger
or symptomatic hematomas require a craniotomy. It is also common
on postoperative complications include increased intracranial pressure.
The injured vessels must be repaired.
– Subarachnoid hematoma: Hematoma that occurs between the arach-
noid mater and pia mater (the subarachnoid space), is a form of seizure
and a fatal medical emergency. It can lead to death or disability even
when diagnosed and treated at an early stage [12]. Subarachnoid hem-
orrhage may occur in cases of TBI in a manner other than secondary
to ruptured aneurysms, being caused instead by lacerations of the su-
perficial micro vessels in the subarachnoid space [4]. If not associated
with another brain pathology, this type of hemorrhage could be be-
nign. It is a frequent occurrence in traumatic brain injury, and carries
a poor prognosis if it is associated with deterioration in the level of
consciousness. [13].
8
2.1.2 Traumatic Brain Injury (TBI)
The prime cause for the intracranial hemorrhage is Traumatic Brain Injury. TBI
(figure: 2.1) is often a result of direct hit to the brain from an external mechanical
force or high acceleration.
Figure 2.1: A detailed description of Traumatic Brain Injury.
The yearly incidence in the US is estimated to be 1.5 per 1000 people (1.3 mild, 0.15
moderate and 0.14 severe injuries) which contributes to 52,000 deaths annually.
About two million people suffer from TBI and about 500,000 are hospitalized for
TBI in the United States alone [11]. In developing countries such as Sri Lanka,
the incidence of TBI has risen due to the alarming increase in automobile use and
9
industrialization with the absence of proportionate development in infrastructure,
and therefore corresponding rise in the number of vehicle accidents.
The mortality rate is estimated to be 21%, 30 days after TBI [11]. It is significant
as large percentage of TBI deaths occur weeks after the event [13], mainly due
to the secondary injury and complications developed. These secondary injuries
exacerbate the damage and contributes to great number of TBI deaths occurring
in hospitals [14].
Outcome for patients with head injury depends heavily on the cause. Patients
with TBI from falls have an 89% of survival rate while only 9% of patients with
firearm-related TBIs survive. Firearms and vehicle accidents are the most common
cause of fatal TBI [11].
2.2 Other diagnosis procedures
2.2.1 Computed Axial Tomography (CAT)
CAT is the definitive tool for accurate diagnosis of an intracranial hemorrhage.
The early detection of the aforementioned blood clots is paramount, and will
be a life saver. Currently only Computer Tomography (CT scan) is capable of
identifying it, nonetheless unfortunately, it is prohibitively expensive and rarely
available. However, a clinical pre-screening technique could improve the utilization
of CT scan.
2.2.2 Magnetic Resonance Imaging (MRI)
A medical imaging technique used to visualize the internals and functions of the
organs and body. MRI offers greater contrast between different soft tissues and
clear picture than CT does, and hence useful in brain imaging. Further, unlike
10
CT, it uses no ionizing radiation, but a powerful magnetic field and therefore less
harmful to body. However MRI cannot be used on patients with metal implants,
and cardiac pacemakers due to effects of the strong magnetic field and powerful
radio frequency pulses [15].
Aforementioned methods share many merits like, high resolution, reliable opera-
tion and excellent accuracy. Nonetheless they also share the following common
drawbacks
• High capital cost and hence unaffordable in developing countries.
• Complex operational procedures and hence require high operational, main-
tenance and repair cost.
• Unportable and hence cannot be deployed with field units to be used for
on-site detections.
In addition, CT scan is highly harmful to the tissues as continuous high power
irradiation could leave the patient with damaged cells thus increasing the likelihood
of cancer and other complications.
2.3 Near Infrared Spectroscopy (NIRS)
Near-infrared spectrum is 0.75 − 1.4 µm in wavelength, defined by the water ab-
sorption. Near infrared spectroscopy is based on molecular overtone and combina-
tion vibrations. Such transitions are forbidden by the selection rules of quantum
mechanics [16]. As a result, the molar absorption in the near IR region is typi-
cally quite small thus it penetrates much farther into a sample than mid infrared
radiation. As a result, near-infrared light can penetrate several centimeters of
biological tissues, enabling noninvasive investigation of the brain from the surface
of the scalp.
11
Near-infrared spectroscopy (NIRS) is an optical noninvasive method of measuring
cerebral hemoglobin distribution. It is a useful technique to investigate biological
tissues, because in the near-infrared regions 750− 900 nm (figure: 2.2), water has
a low absorption, while oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) still
have detectable absorption differences [12].
Figure 2.2: Absorption of light energy by Water and Hemoglobin in NIRregion [6].
It is a relatively simple technique that is portable, does not require a dedicated
technical staff, and does not require the patient to be injected with any isotopes.
2.3.1 Application of near infrared spectroscopy for intracranial hematoma detec-
tion
The basic principle of hematoma detection with NIRS is that water absorption
in the near infrared range is relatively small and hemoglobin contributes to most
of the tissue absorption; extra vascular blood absorbs NIR light more than nor-
mal brain tissue since there is a greater concentration of hemoglobin in an acute
hematoma. By comparing the re-reflected and diffusing optical signal I2 from the
12
suspicious hematoma side and I1 from the healthy side or from a standard model,
the optical density (OD) is calculated as in (Equation 2.1).
OD = log10(I1/I2) (2.1)
The paper “Use of near infrared spectroscopy to identify traumatic intracranial
hematomas” [7] claims that an OD value greater than 0.05 hints the presence of
hematoma and likelihood increases with the OD value. An OD value greater than
0.5 definitely corresponds to an extra cerebral hematoma.
Figure 2.3: Histogram of Optical Density for different hematomas [17].
Figures 2.3 and 2.4 illustrate the direct correspondence between OD, and the
presence and type of hematoma. Hemorrhages, irrespective of their type, depth
or thickness produce an OD > 0.05 at high probability. Further extra cerebral
hemorrhages, irrespective of the depth or thickness produce an OD > 0.6 [18].
Intracerebral hemorrhages fall in the gray region and may be confused with small,
13
Figure 2.4: Optical density variations under different hematoma conditions[17].
extracerebral hemorrhages as well. However as intra cerebral hemorrhage is hardly
operable and tiny hemorrhages are not life-threatening, it can be referred to future
analysis.
2.4 Safety Regulations
Food and Drug Administration (FDA), USA regulations limit the maximum power
irradiated to the skin to 2−5 mW/mm2 in the spectrum of wavelength of 700−900
nm for the exposure of 10 s or more. An LED source of high light intensity can
be considered for use when faced with this limitation.
Based on the experiments by Ito et al. [19], elevations in the temperature due to
NIR absorption is less than 0.5o C. In the experiments by Alper Bozkurt, et el
[20], the increase in the skin temperature was 0.5± 0.1o C due to the cushioning
14
material used to attach the light source, and that was mainly due to skin-air heat
exchange and sweating. However, the temperature increase due to semiconductor
junction is in the range of 1 to 10o C [20]. The major contributor is therefore the
semiconductor junction, which may cause burn injuries [21][22]. Further cell death
is possible in the event of sustained temperature rise above 41o C [23]. Therefore
care must be taken to control temperature.
It is required to control irradiation due to LED to 50 mW/cm2. This is comparable
to the irradiance of the NIR region of the sunlight, which is about 50 mW/cm2.
The temperature increase and total irradiated power can be controlled by pulsing
the current that is fed to NIR LED source.
2.5 Algorithms
2.5.1 Threshold detection
In radar systems, to detect the hit, dynamic threshold is preferred to constant
threshold, because noise level could vary. This ensures the constant probability of
false alarm, while varying the threshold level accordingly.
When standard deviation (σ) and average noise (Vn) can be calculated from the
sample vector, the dynamic threshold is established as in equation:2.2.
Vm = Vn + k × σ (2.2)
2.5.2 Post detection integration
Similar to the aforementioned algorithm, post detection integration is a popular
method used in radar systems. Here, once positive indication vector is generated,
unusual pattern that highlights an impossible situation could be identified.
15
In mathematical terms the probability estimation can be given by equation: 2.3,
for n samples with npositive number of positive indications. Positive indications
with less probabilities can be thus filtered out.
ppostdetectionintegration =npositive
n(2.3)
Complex pattern matching algorithms can be used, and we demonstrate the ap-
plication of bidirectional associative memory in chapter 5.
2.5.3 Bidirectional Associative Memory (BAM)
BAM is an important tool available in Neural Networks and one of the most widely
used. It achieves heteroassociation with smaller correlation matrix. Once trained,
using a set of pairs of vectors, it could recall a pair (A, B) given initial pair (α, β).
Wang et al [24] illustrated the use of BAM for pattern recognition. Also it was
proved by Kosko [25], that BAM will converge for any correlation matrix W.
However it could not guarantee that energy will be at local minimum. But it was
shown by Yeou-Fang Wang et al [26] that guaranteed recall exists for all training
pairs thus trained relationship could be established. Additional mathematical
derivation is provided in Appendix D.
2.6 Chapter Summary
This chapter described background studies. Also it presented the current ap-
proaches and related work. The chapter then continued to explain the application
of NIR in IH detection. After that, it proposed enhancements in detection algo-
rithms to provide a new feature set and to improve accuracy. The next chapter
discusses the operation of iHD system.
16
Chapter 3
SYSTEM OPERATION
The complete solution consists of a portable remote device - iHD terminal - and a
mobile application - iHDMA - running on a mobile computer such as a Personal
Digital Assistant (PDA), notebook or netbook. In this chapter we will look at the
use of the device and operation of both iHD terminal and iHDMA. This chapter is
provided for the completeness only, and for a beginner level instruction User-Guide
must be referred to. Generic overview is given in section 3.1. Section 3.2 discusses
the use of iHD in-depth and section 3.2 leads through the use of iHDMA.
3.1 Using the system
The iHD terminal is used to perform the scans and the iHDMA is used to compute
the results and visualize them.
• Mode 1 - Active Scan : Continuous scan over the injured side of the
head to obtain the intensity values over scanned area.
• Mode 2 - Reference scan : Continuous scan over the healthy side of the
head to obtain reference values.
• Mode 3 - Quick scan : Quick scan is performed in four stages over four
predefined locations (figure: 3.2) on the head. On each location a pair of
scan is performed on each side starting with left side of the head. Realtime
Bluetooth communication is enabled in this method.
17
Figure 3.1: Terminal is operated in four different modes. User can sequentiallymove from one mode to another mode by pressing the mode button on the
device. User is also allowed to skip through modes and come back later.
18
– Mode 3a - Quick Scan on Frontal area : Over left (followed by right)
side of the forehead above the frontal sinus
– Mode 3b - Quick Scan on Temporal area : On left (followed by right)
side of the temporal fossa
– Mode 3c - Quick Scan on Parietal area : Above left (followed by right)
side of the head midway between the ear and the middle of the skull.
– Mode 3d - Quick Scan on Occipital area : Behind left (followed by right)
side of the head midway between the ear and occipital protuberance.
• Mode 4 - Data Transfer : iHD terminal’s Bluetooth module is put in the
listening mode so that a mobile application can query data from the iHD
terminal. Further this mode allows few low level firmware configurations.
Figure 3.2: Although Optical Density scans can be taken at any two identicalpositions on either side of the head, these predefined positions will make the
process simpler and efficient.
Once the scans are completed, the data is transmitted to a mobile computer run-
ning iHDMA for analysis and semi-automatic diagnosis. iHDMA is run on a .Net
platform powered mobile computer with Windows Operating System (OS) in-
stalled. The computer should be Bluetooth enabled. The estimates are computed
by iHDMA and visualized (figures:3.3-3.4).
19
Figure 3.3: An important objective of iHD is to be simple to operate. Theresults are visualized through a brain imaging, and thus provides greater us-
ability.
Figure 3.4: The summary is provided for a quick glance of all the details.
20
3.2 Operation of iHD terminal
Once the iHD terminal is powered on it will be put in mode 1 - active scan. The
user is indicated that the device is ready and waiting for user input. The device is
then moved gently over the injured side of the head from forehead to the occipital
protuberance while keeping the scan button pressed. The active state is indicated
to user.
An average movement of 160 mm is expected at 16 mms−1.On average this will
yield more than 400 samples. If required, it could be performed over shorter length
at a slower speed resulting in concentrated samples for higher resolution.
It is followed by mode 2 - reference scan where identical procedure is repeated
but over the reference side of the head. Should the scan be made over a shorter
length during active scan, it must however be matched here. Although approxi-
mately same number of samples are expected the exact number could vary and it
is considered in the algorithm.
If a scan is repeated under a particular mode before uploading the results to the
mobile application, data will be replaced allowing repeated scans to correct errors
in the process.
To support conventional OD measurements and facilitate the comparisons, mode 3 (3a
to 3d) - Quick scan is provided. Under this mode, the device is kept over the in-
dicated position on the left side of the head and scan is carried out by pressing
the scan button once. It must be followed by the same procedure over the same
location on the right side of the head. For example under mode 3a, firstly the de-
vice is placed on the left side of the forehead above the frontal sinus and scanned.
Then it is repeated on the right side of the forehead above the frontal sinus. Re-
altime Bluetooth connection is enabled under this mode. Similar to mode 1 and 2
repeated scans under a given mode will replace the previous data.
21
Once the required scans are completed, it moves into mode 4 - data transfer.
Here, the device is put into discovery mode searching for a serial profile enabled
Bluetooth connection. If one is found it is added to the list . No more user
interaction is needed on device side to complete the data transfer or diagnosis.
3.3 Operation of iHDMA
The use of mobile application is intuitive and designed to offer optimal user expe-
rience while enabling the maximum flexibility. The application is written on .Net
3.5 platform1and has no other dependency.
The standard flow is to begin with data capture (figure: 3.5). Under this step, user
could verify the presence of a valid iHD terminal by pressing connect button. The
device would respond with a code that will be used to identify a valid terminal.
User could then proceed to capture data.
Figure 3.5: Under capture mode following the device validation user couldimport the data with a single click. Should any error occur data buffer could
be flushed with reset button.
Capture is followed by save option, where record ID, medical officer ID and other
details can be stored together with the data. Also the integrity of the data is
1.Net 3.5 runtime can be downloaded from Microsoft website :http://www.microsoft.com/downloads
22
checked and it is processed (figure: 3.6). The data is stored in XML format so
that it could be subjected to further analysis with external applications.
Figure 3.6: Captured data can be completed with additional parameters andsaved as an XML file. User can then proceed to process data to generate anal-ysis. Also user could load already saved or processed data for a re-analysis.Further availability of active scan, reference scan and OD scan data are indi-
cated to the user; so is the data analysis results.
In the next step a quick snapshot of the results is produced for instant diagnosis.
Both intensity threshold (based on active and reference scans) and conventional
OD results are used to create visual imaging. The color variation is used to indicate
the severity and when the threshold is exceeded alert is generated, thus enabling
the quick identification of a hemorrhage. It is followed by advanced analysis (figure:
3.7).
The last step offers advanced configuration (figure: 3.8) for experienced users. If
Bluetooth connection port is configured with non-default parameters, correspond-
ing settings can be made here. Further signal intensity, pulse frequency and other
related parameters of iHD system can be configured here and synchronized with
the device.
23
Figure 3.7: In addition to quick results, advanced analysis can be opted for.Parameters like normalization count, threshold limit, etc can be set with theaid of visual indication under this section. Hence the parameters can be set to
attain the optimal performance.
Figure 3.8: Mobile computers may offer Bluetooth through a different portor with different settings. Further user might prefer to change iHD terminal’sfirmware parameters for better performance. This section permit such config-urations over Bluetooth, without modifying the actual firmware code. Also it
allows global level iHDMA parameter configurations.
24
3.4 Chapter Summary
This chapter described the use of iHD system. This chapter also described the
detailed use of software (iHDMA) and hardware (iHD) components of iHD system.
The next chapter discusses the architecture design and implementation of iHD
system.
25
Chapter 4
SYSTEM ARCHITECTURE
The critical component of the solution is the iHD terminal. In this chapter we will
look at the design architecture of the iHD terminal. We begin the chapter with
detailed description of the architecture of the iHD terminal in Section 4.1. We look
at the system from bird’s eye view and analyze the inter-operation between the
components. Section 4.2 is dedicated to the hardware design and implementation.
In Section 4.3 we describe the firmware component running the iHD terminal.
Section 4.4 examines the communication protocol specifications.
4.1 iHD System Architecture
iHD terminal is a small hardware platform consisting of a microcontroller, NIR
transmitter and receivers, and other associated hardware units. Detailed explana-
tion is given in section 4.2. The unit operates in four different modes. Detailed
instruction on operating the device is given in chapter 3.
iHD terminal is used to perform different types of scans over both injured side
and reference side of the head, store them and transmit to the remote application.
The fundamental operation of a scan is to transmit a pulse modulated signal and
measure the intensity of the received signal. The transmitted signal is confined to
NIR spectrum with the aid of NIR LEDS, and differential analysis is performed
to cancel ambient noise. This atomic step is repeated at configured frequency on
26
a predetermined pattern to achieve several modes of operation. Further param-
eters pertinent to the signal generated and reception component are maintained
configurable to enable maximum flexibility.
4.2 iHD Hardware Architecture
Figure 4.1: iHD Unit.
The hardware implementation of the system (figure: 4.2) can be grouped into five
basic modules each with unique functionality considering the overall architecture.
The basic modules are,
• Signal generation and Sensor Subsystem
• Processing & Controlling Subsystem
• Communication Subsystem
27
• Power Management Subsystem
• Input Output Subsystem
Figure 4.2: Architecture diagram depicts the interaction between individualsubsystems.
4.2.1 Signal generation and Sensor Subsystem
Concentration of hemoglobin determines the extent of absorption of NIR signal
and thus affects intensity of reception. Hemoglobin concentration in a particular
point of the brain is found relatively by transmitting series of pulses of Near
Infra Red light and measure the reflecting amount of NIR energy, through the
NIR intensity to voltage convertor reading. The amount of reflected energy is a
measure of amount of absorbed energy by brain tissues.
The process can be further divided into the following
• Pulse generation and Current Control
• NIR light emission
28
• Signal reception, noise cancelation and analog to digital conversion
• Interfacing, and Transmitter - Receiver Separation
4.2.1.1 Pulse generation
Burst of pulses are generated through Pulse Width Modulation (PWM) program-
matically. This facilitates the power and time control in software. Microcontroller
is used to generate the PWM signal (figure: 4.3). Although it was initially con-
sidered to use an isolated signal generation circuit for better performance, due
to above reasonable power line noise, microcontroller based signal generation was
chosen. Further it allows greater control over the frequency, duty cycle and burst
time, while maintaining them configurable through firmware settings.
Further external transistor based amplifier is employed to permit larger current
and it acts as a current source. To prevent over current condition, PIC is coupled
through inverters that act as buffers.
Figure 4.3: Burst of pulses that are fed to NIR LED.
4.2.1.2 NIR light emission
The wavelengths selection is based on the absorption spectra of the hemoglobin.
As explained earlier peak difference in absorption between water and blood is at
760nm for Hb and 850− 860nm for HbO2.
29
Wavelength consideration
• 805nm - 810nm : A wavelength of 805nm - 810nm is suitable for hematoma
detection since it is very close to the isosbestic wavelength of oxyhemoglobin
and deoxyhemoglobin absorption, and the signal detected will not be af-
fected by differences in oxygen saturation in blood. Narrow bandwidth high
power NIR LEDs within this frequency range, that were purchased from
ROITHNER LASER TECHNIK are used in iHD.
• 760nm - 850nm : Wavelengths of 760 nm and 850 nm are selected to monitor
temporal changes of cerebral concentrations of HbO2 and Hb. For each
wavelength it is assumed that the linear changes in attenuation for each
chromophore can be linearly summed. The result of these computations is
the value of the absolute change in concentration of each chromophore in
the non-arbitrary units of micro molar of chromophore per liter of tissue.
Two wavelengths can be multiplexed to have separate information on each
wavelength. They can be alternatively turned on and off in pulsed manner
to achieve this.
As the latter approach clearly possess an advantage it was attempted initially.
However due to the fact that the sensor has equal response in both the bandwidths,
mutual noise became critical. It was however later handled by introducing an
optical narrow bandwidth filter at 760nm and 850nm. This produced significantly
better results. Nonetheless, due to the lens attenuation the intensity reception did
not yield results better than single wavelength operation, and thus considering the
simplicity of the design, peak difference in absorption levels between water and
hemoglobin, and ambient noise, 850 nm single wavelength emission is used in iHD
terminal.
One of the most important reasons for choosing a LED is that its radiation is within
the FDA limitations for the radiation power, and if needed multiple LEDs can be
30
operated together to attain required radiation intensity. Further glass top conver-
gence provided by LED was adequate to achieve the radiation intensity required
to penetrate the tissues and skull. Compared with quantitative measurement of
oxygen saturation or other applications, the incident light for hematoma detection
does not require as sharp a spectrum distribution as does the laser; hence LED is
used.
4.2.1.3 Signal reception, amplification and Analog to Digital Conversion (ADC)
Following types of detectors, that can be used to measure the transmitted signals,
were considered.
• Silicon photodiodes (eg OPT101)
• Avalanche photodiodes (APD)
• Photomultiplier tubes (PMT)
• Charge coupled devices (CCD)
The latter two options are not feasible on the basis of price and size.
Silicon Photodiodes (SiPD) : Although silicon photodiodes have a lower sen-
sitivity in comparison to APDs, their reasonable response time, high dynamic
range, and the requirement that several needed to be placed within the flexible
probe area, made them the perfect choice. The integrated combination of photo-
diode and transimpedance amplifier on a single chip (figure: 4.4) eliminates the
problems commonly encountered in discrete designs such as leakage current errors,
noise pick-up, and gain peaking due to stray capacitance.
OPT101 is a monolithic photodiode with on-chip transimpedance amplifier (figure:
4.5) from Texas Instrument (TI) which is specially made for medical applications.
31
Figure 4.4: OPT101. An integrated solution for high sensitive light to voltageconversion over NIR region.
It is inexpensive compared to others such Si sensors and costs only 8USDs [27].
TI’s free sample offer was used for the research and development process.
Figure 4.5: The 0.09×0.09cm2 photodiode is operated in the photoconductivemode for excellent linearity and low dark current. The OPT101 has high sensi-tivity of 0.45A/W and quiescent current is only 120A. Further peak response
is at around 850 nm [27].
Dark Current Offset Correction: The dark current is the result of absence
of light falling on the photodiode and it makes small voltage at the output and it
should be avoided to have better performance.
The photodiode dark current of OPT101 is approximately 2.5 pA and contributes
virtually no offset error at room temperature. The bias current of the op amp’s
32
summing junction (- input) is approximately 165 pA. Further it can is deducted
with the following circuit for improved performance (figure: 4.6).
Figure 4.6: The dark current will be subtracted from the amplifier’s biascurrent, and this residual current will flow through the feedback resistor creatingan offset. The dark output voltage can be trimmed to zero with this optional
circuit. [27].
4.2.1.4 Interfacing and Transmitter - Receiver Separation
The primary concern was to achieve maximum coupling between transmitter and
receiver along the path through cerebrum while ensuring safety, minimum interface
noise addition, and least interference from ambient noise.
Separation : It was noted during the tests, due to the high sensitivity of the
sensor and narrow bandwidth of the transmitter, the direct noise from transmitter
to sensor could prevail at a meter separation even when they are not facing each
other. Further it was noted that the received power increases with the separation
up to 5 cm and then decreases according to inverse square law. The objective of
the design is to ensure maximum coupling through cerebrum and thus the perfect
separation was deemed to be 3− 3.5 cm.
33
Interface : The photon current will be significant and unstable at the contact
boundary between the optical probe and the tissue surface. Poor optical contact
results in noise, false signals, and inconsistent readings. The surface of the head is
usually not flat. Further it was adequate to maintain perfect contact with sensor
while ensuring the emitted power of the LED is tunneled through the skull. It is
achieved by raising the sensor slightly above the surface of the device while placing
a pair of LEDs in a pit with aluminum and black rubber. This guarantees that full
LED power is channeled through head at minimum direct leakage to the sensor
(figure:4.8).
Figure 4.7: The design is planned such that, if necessary this could be furtherimproved to accommodate flexible elastic optical probes as shown.
The flexible probe improves optical contact, but we cannot say that it totally
solves the contact problem; for example, movement of the skin also results in
signal instability [28]. Thus the movement needs to be gentle and we advice a
speed of 16mms−1.
34
Figure 4.8: The LED and photodiode are mounted on the baseboard, whichcan be curved a little to fit the shape of a human head. A black sponge is used
to prevent leakage of light from the source to the detector [28].
Encapsulation : LED cannot be placed in direct contact with skin as the heat
transferred through conduction will be significant and will result in high temper-
ature rise of the skin (up to 9 degrees). This is taken care of by placing the LED
in an abyss while ensuring total enclosure. It requires encapsulation in a suitable
material:
• Flexible enough to conform to the head
• Have suitable optical properties
• Do not give off any by-products
A two-part clear silicon rubber satisfied all of the above requirements. Further,
to prevent direct leakage, it was first wrapped by aluminum foil followed by the
rubber material coated in black color. Encapsulation was carried out in three
stages
• Encapsulation of the back of the sensor board
• Encapsulating the front of the PCB of LED board, except for the regions
directly above the LEDs and photo sensors
35
• Filling the regions directly above the LEDs and photo sensors
4.2.2 Processing & Controlling Subsystem
This is the brain of the blood clot detector and responsible for controlling all
the subsystems. The microcontroller is in charge of the whole operation, which
includes acquiring the signal, adjusting the incident light intensity, and commu-
nicating with the operator. The main functionalities required for this subsystem
will be Analog to Digital Convertor (ADC) to interface Sensor, controlling NIR
LED currents, adjusting intensity via use of pulse circuit, and UART operation
for serial communication between Bluetooth and Microcontroller. Due to the fact
that whole system is operated by low capacity small battery, it should operate
with low power consumption.
4.2.2.1 PIC Microcontroller
The major reason for selection of the PIC microcontroller is that it has emerging
development tools. Other reason for this selection was, this project was selected
to the second phase of the Microchip PIC32 Design Challenge. We were offered
the starter kits and other required resources free. Hence we selected PIC family
of Microcontroller as our main processing and controlling unit.
Features of PIC18F452
• 2 level priority interrupts
• 10 MHz Maximum Frequency
• 32K Program Memory
• Multiple Power Management Modes
36
• UART Module
• 8-Channel 10-bit Analog-to-Digital Converter
4.2.2.2 Sensor Interfacing
The OPT101 sensor produces an analog output which varies among two voltage
levels. But microcontroller processes only digital data. So we need to use analog
to digital conversion when interfacing the sensor to the Microcontroller. The
microcontroller has a 10 bit Analog-to-Digital converter (ADC) with externally
configurable voltage references. The 10-bit ADC includes the following features:
• Successive Approximation Register (SAR) conversion
• Up to 500 kilo samples per second (ksps) conversion speed
• External voltage reference input pins
• One unipolar, differential Sample-and-Hold Amplifier.
The data available from the sensor input can be read as digital after converting
them from analog by using ADC of the microcontroller. The absorption of NIR
light is the difference of the transmitted energy to the received energy. But these
two parameters cannot be calculated in practice.
The basic operation of hematoma detection is by exploiting the fact that water
absorption in the near infrared range is relatively small and hemoglobin contributes
to most of the tissue absorption which is calculated by comparing the reflected
and diffusing optical signal intensity Ileftfrom left side and Iright from right side,
the optical density OD can be derived to:
OD = log10(I0/Ileft)− log10(I0/Iright) = log10(Ileft/Iright) (4.1)
37
Then intracranial hematoma can be detected by comparing the OD with the de-
tection threshold and historical data. According to position, different intracranial
hematomas can be divided into epidural, subdural, and intra-cerebral types. Back-
ground study given in section 2.3 provides the insight into this.
4.2.2.3 Possible methods of computation
• Time resolved : In Time-resolved method, short pulses of light is trans-
mitted and the distribution of time of flight of the transmitted photons is
measured. This measurement provides the greatest amount of information
about the tissue being investigated, but the device will be complex.
• Frequency modulated : Frequency modulated instruments involve mod-
ulating the light source at radio frequencies and detecting the intensity and
phase of the transmitted signal. This requires less complex instrument.
• Continuous Intensity : The continuous intensity instrument is the sim-
plest of all, where light is injected into the tissue and the attenuated trans-
mitted intensity is measured at some distance from the source.
The exact depth of the hematoma from the surface that can be examined by NIRS
is still controversial. Further direct depth calculation is rendered impossible due
to short distances involved.
The typical time of flight for a depth of 3cm will be
Tflight = (2× 0.03)/(3× 108) = 2× 10−10s (4.2)
We will neither be able to generate nor detect at this high frequency (Equation
: 4.2) in a microcontroller based implementation. However through transmitting
38
burst of pulses of continuous intensity (type 3) and detecting patterns the charac-
teristics of the hematoma can be found.
4.2.3 Communication Subsystem
Figure 4.9: GS-BT2416C2 Bluetooth Module
The use of a remote connection to a wireless device such as PDA can help us
integrate great variety of flexibility into the system. The mobility of the device
is extremely high in such a design. Also it increases user friendliness by use of
graphical user interface on the remote device. Reasons for use of a Bluetooth (BT)
module (figure: 4.9):
• Cost effective comparing to high-end microcontrollers and graphical display
on the device itself
• Easy integration with microcontroller via Universal Asynchronous Receiver
Transmitter (UART)
39
• Relatively flexible, feature-rich software development and better graphic
quality are achieved. With use of BT we can maintain our functionality
in better quality and in very economic manner than other options.
A suitable BT module called GS-BT2416C2 is used as it possess the following
characteristics.
1. SPP (Serial Port Profile) Support : All Mobile phones/PDAs support SPP
only
2. Direct interfacing to a UART and control by AT commands
3. After initialization with AT commands it direct data transfer via UART
4. Bluetooth specification V.1.2 compliant
5. Transmission rate up to 721 Kbps
6. Working distance up to 10 meters without an external antenna
7. Hardware based UART flow control
The BT module is compatible with UART interface controlling (figure: 4.10). The
module with AT command is dedicated to implement serial cable replacement. An
automatic point to point connection takes place when modules are switched on.
Modules are configured via macro instruction to play the role of master or slave.
4.2.3.1 UART
UART is used to control the module with AT commands (COMMAND MODE),
or send/receive serial data to be transmitted over the SPP Bluetooth link (DATA
MODE). The first time that the module is powered up, the default UART settings
are as following (saved in flash memory)
40
Figure 4.10: The interfacing of the BT module using the UART and 2 GeneralPurpose Input Output(GPIO) ports.
1. Baud rate: (bps) 9600
2. Data bits: 8
3. Stop bits: 1
4. Parity: None
5. Flow control: None
When these settings are changed by the AT+UARTSETUP command, they are
stored in the flash memory to be reloaded when the module is powered up the
next time.
GPIO1 : This will be configured as output. GPIO1 is high when an SPP Blue-
tooth link to a remote device is present. GPIO1 is low when no Bluetooth link is
present.
GPIO3 : The GPIO3 should be configured as input. If GPIO3 is set to high,
the module switches its mode of operation to DATA MODE. If GPIO3 is set to
low, the module switches its mode of operation to COMMAND MODE. After
aforementioned initialization is performed, any feature of the BT module can be
41
accessed via the its protocol stack (figure: 4.11). The dataflow and command sets
are explained under section 4.3.
Figure 4.11: Bluetooth Stack
4.2.4 Power Management Subsystem
This subsystem manages power to the rest of the subsystems. To maximize the
power capacity and portability a rechargeable dual cell (7.4V - 8.4V ) will be used
to power the terminal. Provision of power at the appropriate voltages to the
other subsystems will be handled by this subsystem. The voltage and current
requirements for the device is tabulated below (Table 4.1).
Device / Subsystem Vmin (V)Vtypical(V)Vmax (V)Imax (mA)
PIC18F452 2.3 3.3 3.6 200Bluetooth Module 3.13 3.3 3.47 90Sensor Subsystem 2.7 5 6 250
I/O 3 5 6 50
Table 4.1: voltage and current requirements of iHD terminal subsystems.
4.2.4.1 Low Dropout (LDO) Regulators
The REG1117 is a family of easy-to-use three-terminal voltage regulators from
Texas Instrument. The family includes a variety of fixed and adjustable-voltage
42
versions, two currents 800mA and 1A. REG1117 characteristics
• Output tolerance: 1%
• Output Current: 800mA, 1A
• Dropout Voltage: 1.2V @ 800mA
• Internal Current Limit
• Thermal Overload Protection
• 0.06V voltage ripple at 3.3V Output
• 0.10V voltage ripple at 5.0V Output
4.2.4.2 Power Source
The requirement to have portability of the terminal requires small size battery op-
eration when considering the weight and the size of the device. Therefore we have
selected rechargeable dual cell with capacity of 2100mAh for major power source
for our device. The 2100mAh of capacity can operate the device continuously for
3 hours at full functionality.
4.2.4.3 DC Power and Charging
The device can be simultaneously powered by both battery and external DC sup-
ply. A generic purpose DC adapter with 9V , 1A rating could be used to charge the
batteries while powering up the terminal. When the device is discharged the user
can connect to the charger and keep until the batteries get fully charged. However
to minimize the complexity and size of the device, no special power management
unit is provided.
43
4.2.4.4 Sleep, Power Saving Modes
The iHD terminal is an on-demand-use device and hence not expected to be pow-
ered on when it is not in use. Therefore special power saving and sleeping modes
other than the micro controller’s built in power saving modes of operation are not
considered.
4.2.5 Input Output Subsystem
This is the subsystem that interacts with user. The Sensor Subsystem could also
be categorized under this subsystem, but as it is the most critical component it
was considered separately. In this section we focus on controlling inputs to the
systems.
Considering the portability, power and simplicity requirements, we moved the
responsibility of processing and displaying results to the mobile computer and
therefore iHD terminal is only providing minimal user interaction.
The main inputs used here to interact with user, are the mode selection button
and scan button. Visual imaging, statistical analysis and learning system will be
implemented in remote mobile computer such as a PDA which will act on the data
received via the Bluetooth connection.
As explained in section 3.1, once the device is powered on it will be put in mode
1. Through a push button, user could navigate through modes. The button is
connected to an interrupt enabled input of the microcontroller to give the highest
priority. User can then trigger the scan by pressing and holding the scan button
(under mode 1 - active scan and mode -2 reference scan) or pressing and releasing
the scan button. The button is provided with de-bouncing algorithm to improve
user interaction and allow accidental releases.
44
4.2.6 Safety concerns
As explained in section:2.5, care must be taken to ensure that the irradiated power
is within the safety limits. It was also earlier established that radiation up to 50
mW/cm2 is harmless as it is comparable to the irradiance of the NIR region of
the sunlight.
The average radiated power of an NIR LED is 20 mW, thus resulting in total
radiation of 40 mW assuming that 1 cm2 area is illuminated.
4.3 iHD Firmware Architecture
Figure 4.12: iHD firmware block diagram
iHD Firmware Architecture is shown in (figure: 4.12). Due to the realtime nature
of the application both external input and internal clock triggered interrupts based
scheduler is implemented to switch between different states. A user input will take
priority and hence result in the state change wherever the program was. However
necessary care is taken to windup the current state properly and variables are
properly stored.
45
State maintainer chooses the appropriate state function and runs. There are two
such state implementations. In addition, it handles the user indication output
that reflects the current state and status of the operation.
4.3.1 Communication state implementation
This module handles the Bluetooth communication. When the state is entered,
the component resets the BT module through hardware reset, thus escaping from
whatever status the BT module is currently in. It is followed by initialization of
AT commands, and initial settings. Then BT is put in discovery mode, and when
it is contacted by a mobile computer running iHDMA it answers. This module
is also responsible for the responses to the commands sent from iHDMA. In this
context, it handles the formatting of replies and queueing them.
4.3.2 Data Acquisition state implementation
This module handles the different modes of scans. When one of the scan mode is
entered, settings of an atomic scan operation is loaded from EEPROM and stored
in global variables. Then the module chooses the properties depending on the scan
mode and calls the atomic scan function that is common for all. The operation is
then delegated to PWM generation, ADC sampling, encoding (10 bits output to
a word) and store functions.
4.3.3 User Indication IO
The current state is displayed to user at all time, and changes in states are reflected
instantly, irrespective of the unloading and loading latency involved. Further, the
46
operation status such as scanning, busy, ready to scan and errors are displayed
appropriately.
4.4 Communication Protocol
This section describes the protocol that is adopted to be used for the commu-
nication between the remote intracranial hematoma detector (iHD) and Medical
Application (iHDMA) that is running in a Personal Computer, Notebook or Mo-
bile Computer. Detailed description of the protocol and examples are given in
Appendix B.
4.4.1 Introduction
The iHDMA will always be a host and iHD always be slaves. Therefore, only
iHDMA can initiate communication by querying the iHD and the iHD is obliged
to respond. The atomic unit of the data is a word (16 bits) which is in big-endian
format, i.e. the higher order byte is sent first. All the messages are encapsulated
between a message-starting header and message-ending header. Further frame
headers wrap all data frames.
One cycle of communication includes the command initiated by iHDMA and the
reply from iHD.
Commands can be one of the following :
• Request Settings
• Update Settings
• Request Data
• Ping
47
4.4.2 Structure
As aforementioned, a command or reply message is wrapped by headers. The
body length can be of variable number of words.
The header identifies the type of the word (i.e. a Message Header) and type of
the command (Ping, Request Data, Request Settings or Update settings), and the
body is supplemented with any parameters if available. Hence, the body may be
absent when it is neither required by the command nor available. The command
or reply is ended with message-ending header which is 0xFFFF for all messages.
4.4.3 Message Starting Header
A 16 bits long word of the following format (Table : 4.2) will serve as a message-
starting header.
1 1 1 1 0 0 0 0 0 0 0 0 X X X XWord Type Reserved Command / Message ID
(Message Header) (0 always) Reply
Table 4.2: Message Starting Header
There are four types of commands (Table : 4.3) and they are replied with the
reply bit turned on:
Ping 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1Request Data 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0Request Settings 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0Update Settings 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0
Table 4.3: Types of message commands
48
4.4.4 Message Body
For a command, message body is used to pass relevant parameters. For a reply,
message body consists of information that is queried through the command and
can vary depending on the type of the command.
4.4.5 Message-ending Header
All 16 bits are set to 1s and thus can be easily identified. Hence, data packets of
any type, settings-replies, frame headers and ping-replies cannot have all 16 bits
set.
4.4.6 Frame
A frame can be either a frame-idle or data-frame, again consists of frame-starting
header, data packet and frame-ending header. Scan data is sent inside a frame
when requested.
4.5 Summary
This chapter described hardware architecture of iHD terminal explaining the in-
dividual subsystems and their operations. It discussed the firmware architecture
of iHD terminal. Also it presented the communication protocol specification. The
next chapter discusses the algorithms used to estimate the likelihood of hematoma
in iHDMA.
49
Chapter 5
HEMATOMA LIKELIHOOD ESTIMATION IN IHDMA
Traditionally, for optical density calculations, scans are performed at selected pair
of locations, and they are used to estimate the likelihood of hematoma. We explore
alternative algorithms, mainly derived from a completely different area - radar
systems, for this purpose. In addition, we try to develop Artificial Neural Network
(ANN) based model for semi-automatic detection.
An introduction into optical density calculations is given in section 5.1 with a
short discussion on its merits and demerits. Section 5.2 examines the characteris-
tics of the intensity sample values. In section 5.3 we generate one-to-one reference
estimates for each scan. Section 5.4 walks through threshold detection based esti-
mation followed by post detection integration in section 5.5. Section 5.6 discusses
the possibilities of using complex pattern matching algorithms and explains appli-
cation of Bi-directional Associative Memory (BAM) based ANN model.
5.1 Optical Density calculation on Isolated Scans
As explained in section:3, mode-3 corresponds to quick scans on isolated pre-
defined locations on either side of the head. The scans are performed in pairs
starting from the point on the left side of the head followed by the measurement
at the corresponding point on right side. It leaves us with four pairs of intensity
measurements. Using equation:4.1, we could estimate OD for the given point.
50
Absolute value of OD tells us the likelihood of a hematoma. The background
study given in section:2.5, provides us with a method of ascertaining it.
The sign of OD gives us the hematoma side. A positive OD means right side’s
intensity value is lower, and it translates to higher absorption of NIR on that side.
Hence it could be inferred that hematoma is on the right side.
Scans are performed on four predefined locations namely, frontal area, temporal
area, parietal area and occipital area. When a positive indication is generated more
scans can be carried out in the proximity and a clear picture can be generated.
5.1.1 Merits and Demerits
The merits lie in the simplicity of the method and quick analysis. It gives the
results instantly, and an expert could offer the opinion instantly. Using his past
experience, he could be able to make the estimations without further analysis.
The demerits are possible false alarms due to noise and glitches that are not
accounted for. Further the absence of visual indication, advanced analysis and
intelligence makes this method unsuitable for less experienced personnel such as
first-aid crew.
5.2 Characteristics of Intensity values and OD
Hematomas are generally of considerable size and hence identifiable from several
scans close by. Sensor, transmitter separation of 3 cm browses through an area of
1cm2 and hence by taking several measurements around that point false alarms
and misses can be avoided.
With regard to the absolute value of OD, an OD > 0.5 corresponds to 3 times
higher absorption and thus confirms the presence of hematoma. However an OD =
51
0.1 corresponds 1.26 times higher absorption which could have been caused by a
glitch or noise. An expert opinion is hence required under this method. Through
ANN we propose and automatic detection in the absence of an expert.
Further, the ability to make continuous scans give a promise for visualizing the
results easily. This requires us to acquire a number of samples over the injured
side of the head and find the corresponding reference value.
5.3 Reference Estimation through normalization
The caveat in carrying out continuous scans is the difficulty in controlling the
number of samples and uniformity. We could advice the personnel to move at a
constant slow pace over the head thus achieving reasonable level of uniformity in
the measurements. Further, knowing the total number of samples and end points
on the head we could estimate the average length covered by each sample.
However the problem of unequal number of samples from active scan and reference
scans still exists. We handle this by normalizing the reference sample count to that
of active scan.
5.3.1 Moving average based normalization
For N number of active scan samples and M number of reference scan samples,
our objective is to achieve N number of reference scan samples (figure: 5.1 and
5.2) thus obtaining one-to-one correspondence between active scan samples and
reference scan samples. This is done by the normalization function (equation:
5.1).
52
Figure 5.1: The image depicts a possible scenario where unequal number ofsamples might be acquired in Active and Reference scans.
Figure 5.2: The image illustrates the normalization operation. While unequalsample counts have been normalized, spikes in reference samples also have beenbeen removed. This smoothing effect is controlled by the Normalization Factor
and in realtime controlled by the slider shown in the diagram.
53
let, X be the active scan sample vector, Y be the reference scan sample vector,
and Z be the normalize reference scan sample vector.
for1 ≤ i ≤ N,
j = bi×N/Mc
Ns =
j − L if j − L > 1
1 if else
Ne =
j + L if j + L < M
M if else
Z[i] =
∑Nek=Ns Y [k]
Ne−Ns(5.1)
However, with the distance from the current sample, the influence of other sample
values must decrease. That is if there are 20 active scan samples and 40 reference
scan samples the normalized reference scan sample corresponding to the 12th active
scan sample must heavily depend on 24th reference scan sample than on 26th or
22nd samples.
This is achieved by a windowing function, for example, a linear windowing function
would be as in (Equation: 5.2).
W1 =
k−Nsj−Ns
if Ns ≤ k ≤ j
Ne−kNe−j
if j ≤ k ≤ Ne
0 if else
(5.2)
Z[i] =
∑Nek=Ns Y [k]×W1
Ne−Ns(5.3)
However, for a larger normalization count along with a higher number of samples,
we go for an inverted-square windowing function (Equation: 5.4), that takes the
54
shape of 11+x2 .
W2 =
11+(k−j)2
if Ns ≤ k ≤ Ne
0 if else(5.4)
5.4 Threshold detection
Once we generate two vectors of equal size, they can be directly compared and
anomalies can be identified. However to standardize the process and automate we
have to create a mathematical model.
The caveat is the intensity variation due to absorption, range from 1.2 times to
as high as 30 times. Adopting log model solves the problem by linearizing the
variation according to the order of the magnitude. Nonetheless, both linear and
logarithmical values are reported by iHDMA for completeness.
Further, the hematomas can be identified by setting a fixed threshold, for example,
OD = 0.2. However, this could cripple the flexibility and extensions. Therefore we
propose a probability based detection algorithm which will report the probability
of hematoma on individual locations based on normalized reference scans and
historical data.
Considering the reference vector as the sample set, standard deviation is found.
For a given probability of false alarm (pFA) dynamic threshold (figure: 5.3) is
set. The outliers from set of reference scans (the samples exceeding this dynamic
threshold) are reported in terms of both OD and excess in times of standard
deviation (zstd).
55
Figure 5.3: In threshold detection, dynamic threshold is generated by main-taining constant probability of false alarm. A similar approach is taken and theThreshold Factor is in realtime controlled by the slider shown in the diagram.
Figure 5.4: Results after threshold detection.
56
Figure 5.5: Post detection integration is used to improve the accuracy, andautomating detection. The key issue is to remove improbable scenarios similarto one shown in figure: 5.4). As it can be seen from the above image, thediscrepancy is removed through post detection integration using algorithms such
as BAM.
5.5 Post detection integration
The certitude of the presence of hematoma can be further enhanced by considering
the neighboring samples. Because hematoma cannot exist as an isolated tiny dot,
principle of locality could be applied to identify anomalies.
The simplest of all is to select a set of n samples (typically n = 5) around the
sample of interest (n− 2 to n + 2) and count the positive indications. If number
of positive indications are greater than 50% or a given constant, the indication
ppostdetectionintegration could be ascertained (Equation:2.2).
This is coupled with the parameters derived earlier (either OD or standard devi-
ation) to give a better estimate (Equation:5.5).
57
Ihematoma = ppostdetectionintegration × zstd (5.5)
5.6 Application of Bidirectional Associative Memory (BAM)
While aforementioned probability based approach could yield satisfactory results it
could be easily misled. For example, a vector of [1, 0, 1, 0, 1] could make the system
ascertain a positive indication, while it is clearly recognizable that an alternate
zeros and ones can mean some errors and not the presence of a hematoma.
As introduced in section:2.5.3, BAM is an ANN model that can be used to detect
patterns with machine learning. This could enable us to estimate the existence of
hematoma with higher accuracy. It can be used to infer the presence of hematoma
on a selected set of samples by learning from experience.
Let, p1×n be the vector of selected samples and typically P1×n.
We generate BAM vector Wn×n by,
W =R∑
r=1
rqrtrpT (5.6)
Where,
q is a constant found to minimize the energy of W and,
t is the expected results generated from the past experience.
Following the method, explained in section:2.5.3 presence of hematoma can be
identified when a complete recall is available. However it must be noted BAM,
being a semi-automatic algorithm using ANN, destroys the probability information
prevailed thus far, and hence it would not be possible to generate the probability
of existence further down.
58
5.7 Summary
We began the introduction by revisiting OD calculations. We also looked at the
merits and demerits. Then we analyzed the characteristics of intensity values and
established that continuous scan improves the outcome. We looked at few algo-
rithms to normalize the reference count so that one-to-one mapping can be made
between reference scan samples and active scan samples. We then introduced the
application of threshold detection followed by post detection integration. Lastly,
we discussed the use of an ANN model such as BAM for pattern matching. The
next chapter presents the evaluation of the iHD system and discusses the results.
59
Chapter 6
RESULTS, EVALUATION AND DISCUSSION
In this chapter we discuss the clinical trial procedures, output of the individual
components, performance metrics, and presentation of results.
6.1 Testing procedures and clinical trials
6.1.1 Trial Objectives and Purpose
The purpose of the trial is to assess the efficacy of the iHD and to obtain the
statistical data of the area and concentration of haemoglobin in intracranial haem-
orrhages.
6.1.2 Trial Design
The patients undergoing surgery, due to an intracranial hematoma are intended
to be used in the trial procedure. The trial procedure will happen between the
events of taking the CT scan of the patient, and the starting of the surgery.
To avoid bias, all the patients undergoing surgery due to intracranial hematoma
within a specified time period (from 5pm 21st May to 8am 22nd May), will be
taken for the trial.
60
The expected duration of subject participation is 5 minutes before undergoing
the surgery and a follow up trial after the surgery is conducted with a duration
of another 5 minutes, when possible. All patients undergoing the trial will be
scanned with the iHD. There will be 2 areal scans done in each side of the head
of the patient, and another 8 single scans on 8 specific areas of the head.
The discontinuation criteria for this trial is, if the patient shows signs of external
bleeding, or is the injured side of the patient cannot be touched without causing
pain to the subject.
The iHD project team will be accountable for conducting and managing the trial
procedure.
6.1.3 Selection and Withdrawal of Subjects
• Subject inclusion criteria: The patient should be suspected of having an
intracranial hematoma. The patient should be undergoing surgery.
• Subject exclusion criteria: The patient having an external bleeding. The
patient being touched on the injured side will result in affliction.
• Subject withdrawal criteria: The patient having an external bleeding. The
patient being touched on the injured side will result in affliction.
6.1.4 Treatment of Subjects
• The patient will be scanned in the injured side.
• The patient will be scanned in the opposite side.
• Singular scan values are obtained in 8 different locations in the head shown
in figure:.
61
6.1.5 Assessment of Efficacy
• Specification of the efficacy parameters: The following will be the main
parameters that would be used to measure the efficacy of the device.
– The difference in the optical density on the injured side and the refer-
ence side.
– The correlation of the area of the hematoma from the CT scan and the
detected region by the iHD.
• Methods and timing for assessing, recording, and analyzing efficacy param-
eters: The efficacy parameters will be stored in electronic format, as an xml
file, and will be analyzed later by the iHD project team.
6.1.6 Assessment of Safety
The safety regulations defined by FDA and discussed in section 2.4 have been
followed in the design as illustrated in section 4.2.6, and will be abided by in
conducting the tests as well.
6.1.7 Statistics
All the patients that satisfy the trial inclusion criteria at the Accident ward, Na-
tional Hospital, Colombo during the time period mentioned will be used for the
trial, and no sampling method will be used.
The number of subjects planned to be enrolled is 20. This number is used consid-
ering the average rate of reported cases of intracranial hematomas in the National
hospital and the statistical significance.
62
Data Handling and Recordkeeping : All the data of the trial will be kept and
handled by the iHD project team, until they are published.
Publication Policy : The data and records of the trial will be published by the
iHD project team, at the completion of the trials.
6.2 Evaluation and Discussion
6.2.1 Sensor outputs
Light to voltage converter generates on average 700 mV when NIR light is sent
through the healthy side of the skull at frontal area. This is comparable with the
ambient noise of 50 mV . Therefore, the device is capable of detecting maximum
of 1.15 in OD.
On an unshaved head, the results have not been satisfactory and average results
ranged between 100 mV and 150 mV . As the hair absorption is significant, it will
be necessary to increase the power radiated. Therefore the device that is tuned
for the use on a shaved head will be ineffective when there is hair absorption, in
which case greater power is needed.
ADC has a resolution of 10 bits with reference voltage of 5V. Hence average sen-
sor output corresponds to a maximum of 143 of 1024 steps available, and there-
fore 8 least significant bits of 10 bit ADC output will be sufficient to detect the
hematoma. Therefore on continuous scan only last 8 bits are stored for analysis.
6.2.2 Performance metrics
The complete process takes less than 15 minutes. Single scan takes on average
5 seconds and programmatically it is ended in 10 seconds. Further delays are
63
enforced to avoid accidental changes. Transfer of data from iHD to iHDMA over
Bluetooth is almost instantaneous. Full set of calculations in iHDMA on a note-
book with pentium AMD Turion 2.0 GHz processor and 2GB memory running
Vista OS takes less than 2 seconds.
The terminal consumes on average 700 mA during operation. A pair of lithium-
ion dual-cell can feed the device for more than 3 hours continuously. However
the device will be put in standby mode and consumes lesser power normally. In
practical situation, this translates to more than 30 scans per recharge.
6.3 Presentation of results
Firstly, acute differences in neighborhood and unequal sample count of reference
scan samples are accounted for through normalization function.
Figure 6.1: Reference samples before normalization
Threshold detection is performed on the log ratio between scan and reference scan
values.
64
Figure 6.2: Reference samples with normalization factor 4
Figure 6.3: The image illustrates the effect of normalization factor in smooth-ing reference scan values.
Final results are also visualized to provide a better interpretation.
65
Figure 6.4: The image depicts the effect on using different functions for nor-malization.
Figure 6.5: The image depicts the effect on using different functions for nor-malization.
66
Figure 6.6: Threshold detection is performed on the log ratio between scanand reference scan intensity values. The image shows the OD calculation.
Figure 6.7: The image demonstrates the use of BAM to filter out unlikelydetections semiautomatically by matching patterns.
67
Figure 6.8: The image shows the visualization of the results on a subject withinjury on left side of the head. Intensity variation on the injured side is shownby the red color component. Further positive indications that are confirmedby post detection integration - i.e. using BAM - are marked by white colored
circles.
Figure 6.9: Quick scan is used to calculate the OD values on predefinedlocations for quick analysis by an expert. Under this mode, detection is made
manually from OD values.
68
Chapter 7
CONCLUSION
The report discussed the concepts behind the design of intracranial hematoma
detector, implementation, development of algorithm for semiautomatic detection,
and evaluation.
Intracranial hematoma detector is a non-invasive system to detect the presence
of hemorrhages in brain with above reasonable accuracy semiautomatically. We
summarize design challenges, contributions of the project and future directions for
enhancement.
7.1 Challenges
We faced numerous challenges in designing the system, developing detection algo-
rithms and evaluating the results.
• Power and safety: NIRS is a non-invasive detection technology used for
brain imaging. However, the potential temperature rise due to heat transfer
limits the maximum power that can be used. Although the primary transfer
mode is conduction that can be prevented by proper isolation, radiated heat
also plays a considerable role - 0.5oC at 50 mW/cm2, that limits the power
rating of the LEDs used. It must also be noted that solar radiation at NIR
wavelength is of the same order, and hence contributes to significant noise.
The dilemma is to increase received signal power, while keeping the source
69
power rating within the limits regulated by FDA. It is mainly achieved by
pulsing the source current, and producing NIR light in burst of pulses which
results in greater peak power while keeping the average power below the
limit.
• Compactness of the device: The size and design of the iHD should be
portable as it is expected to be a handheld. The circuits are arranged in
layers to stack them in smaller space. Also mobile phone batteries are used
to provide enough power while saving space.
• Detection and false alarm: The ability to detect a hematoma is inversely
proportional to the probability of false alarm. Hence, the correct balance
must be attained between both, and it is achieved by maintaining a variable
factor, that is adjusted semiautomatically.
• Field testings: The efficacy of the device can be only tested when it is
deployed in real world scenario. The difficulty is in obtaining the consensus
from the patients and relevant medical officers, and infrequency of arrival of
patients with extracerebral hematomas over a short time span. The testings
will be done.
7.2 Contributions
The report discussed the design and implementation of intracranial hematoma
detector - iHD, and accompanying mobile application - iHDMA. Also it listed out
the challenges faced in developing the solution. We itemize the contributions of
the report below.
70
• Hardware device: iHD terminal is a simple microcontroller based system
that uses bursts of NIR light energy as per the specifications that are par-
tially configurable to measure the absorption differences. The system inter-
connects several subsystems including NIR transmitter and sensor, measures
the intensity of NIR light at reception, stores the data and transfers to the
mobile application.
• Continuous scan: In contrary to conventional methods of taking mea-
surements at particular points on the head and calculating OD, we adopted
a novel way of using continuous scan from radar systems. It enables us to
provide a visual imaging and a more descriptive picture of the details.
• Semiautomatic detection: To assist less experienced personnel, espe-
cially when it is used in field operations in the absence of experts, semiauto-
matic detection is developed. Here,threshold detection - another derivation
from radar systems, is used with variable threshold factor.
• BAM based filtering: The detection might include improbable targets.
With machine learning, however, we can filter out them. BAM is an artificial
neural network based tool that is used to detect patterns. BAM is adopted
here for the aforementioned post detection identification.
7.3 Future directions
Although the system if fully functional, it could be enhanced in usability, accuracy
and feature set.
In our discussions with neurologists, we understood they desire better imaging fea-
tures from this solution to substitute CT scans in peripheral hospitals. Currently
the system scans along a line, i.e. linear in dimension. By implementing an array
of LEDs and sensors it could be developed into a two-dimensional solution, which
could generate a complete visual imaging of the brain.
71
The device can be made more compact, resembling a mobile phone device by
using smaller surface mount components and advanced materials for chassis such
as recyclable glass and aluminum.
In order to keep the design simple and elegant it was decided to off-load all re-
porting functions to a mobile application. However, calculations such as OD value
can be moved to iHD.
Also to guarantee greater accuracy the signal can be modulated by a predefined
frequency and at the reception a matched filter can be used to extract the signal
from ambient noise. It might result in more accurate detection in the presence of
significant ambient noise such as direct solar radiation.
72
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76
Appendix A
IHD SCHEMATIC
Figure A.1: Schematic diagram of iHD terminal. Signal generation and sensorsubsystem and Communication subsystem are shown in the diagram.
77
Figure A.2: Schematic diagram of iHD. Microcontroller and power manage-ment subsystem are show in the diagram
78
Appendix B
COMMUNICATION PROTOCOL SPECIFICATION
Version : 0.1
The document describes the protocol that is adopted to be used for the commu-
nication between the remote intracranial hematoma detector (iHD) and Medical
Application (iHDMA) running from a Personal Computer, Notebook or Mobile
Computer. Please note “iHD” and “iHD terminals” are used interchangeably to
denote the device.
The following media are currently supported without modifications
• Bluetooth
• Serialport
In addition, other media may be supported, but have neither been tested nor docu-
mented.
B.1 Introduction
The system is planned to accommodate multiple devices non-concurrently, and
hence uses Host-Slave architecture. The iHDMA will always be a host and iHD
will always be a slave. Therefore, only iHDMA can initiate communication by
querying the iHD, and the iHD is obliged to respond.
79
The atomic unit of the data is a word (16 bits) which is in big-endian format, i.e.
the higher order byte is sent first.
All the messages are encapsulated between a message-starting header and message-
ending header. Further frame headers wrap all data frames.
One cycle of communication includes the command initiated by iHDMA and the
reply from iHD.
Commands can be one of the following
• Request Settings
• Update Settings
• Request Data
• Ping
B.1.1 Structure
As aforementioned, a command or reply message is wrapped by headers. The
body can be of any number words long.
Typical command or reply message
Figure B.1: The header identifies the type of the word (i.e. a Message Header)and type of the command (Ping, Request Data, Request Settings or Update set-tings), and the body is supplemented with any parameters if available. Hence,the body may be absent when it is neither required by the command nor avail-able. The command or reply is ended with message-ending header which is
0xFFFF for all messages.
80
B.2 Message-starting Header
A 16 bits long word of the following format will serve as a message-starting header
:
Figure B.2: A 16 bit word serving as a message-starting header.
There can be four types of commands :
Figure B.3: Four types of words used for message starting header of com-mands.
And the corresponding replies are as follows :
Figure B.4: Four types of words used for message starting header of replies.
B.3 Message Body
For a command, message body is used to pass the relevant parameters.
For a reply, message body consists of information that is queried through the
command and can vary depending on the type of the command. All possible
message body combinations are explained below.
81
Figure B.5: Message body of ping reply. Ping reply can be anything, norestriction is imposed on its format, but still it has to be divisible by 3.
Figure B.6: Message Body of update settings and request settings reply.Replies are the same and both return all setting parameters, one per word(Settings reply can be anything, no constraints on its format, but it cannot be
0xFFFF at any time to distinguish from Message-ending Header).
Figure B.7: Message body of request data reply. Data is packetized one levelfurther into frames. A typical request data - reply will be of this format, hence
making the frame a packet.
B.4 Message-ending Header
Figure B.8: Message-ending Header. All 16 bits are set to 1s and thus easilyidentified. Hence, data packets of any type, settings-replies, frame headers and
ping-replies cannot have all 16 bits set.
B.5 Frame
A frame can be either a frame-idle or data-frame
Figure B.9: Frame-idle.
82
Figure B.10: Data-Frame consists of a Frame-starting header, data packets,and a frame-ending header.
B.6 Example Word Patterns
Figure B.11: Typical word. The first 4 bits of a word are used to identifythe type of the word. This is possible because core information is of maximum10 bits long (in the case of ADC output) and thus avoids unnecessary escaping
and processing that will be otherwise needed.
B.6.1 Word Types
Determined by the first 4 bits of any word.
1. Message header (1111)
2. Frame Header (1011)
3. Frame idle (1101)
4. Data Packet (0000)
In addition, special word - Message-ending header (0xFFFF) will be determined
by the whole word (all 16 bits)
83
B.6.2 Message ID
Determined by the last 4 bits of the message-starting headers.
1. Ping (0001)
2. Request Data (0010)
3. Request Settings (0100)
4. Update Settings (1000)
B.6.3 Command or Reply
Determined by the 3rd bit of the second word in a message-starting header.
1. Command (0)
2. Reply (1)
B.6.4 Frame ID
Determined by the last 4 bits of the frame-starting header.
1. Real time scan (0001)
2. Real time calibration (0010)
3. Stored OD Values
(a) ODa (0100)
(b) ODb (1000)
(c) ODc (1100)
(d) ODd (1110)
84
B.7 Summary
This protocol is designed with the scenario of multiple iHDs communicating with
iHDMA non-concurrently but simultaneously. Further, a typical communication
pattern of Pinging device, Request settings, Update settings, and Data acquisition
is assumed. The packets are formed from the atomic unit of word (of 16 bits with
first 4 bits identifying the type of the word).
85
Appendix C
DATASHEETS
Figure C.1: Datasheet of PIC 18F452 - the microcontroller used in iHD ter-minal.
86
Figure C.2: Pin diagrams of PIC 18F452 - the microcontroller used in iHDterminal.
87
Figure C.3: Specifications of OPT101 - the light to voltage converter used iniHD terminal.
88
Figure C.4: OPAMP characteristics of OPT101 - the light to voltage converterused in iHD terminal.
89
Figure C.5: Datasheet of 760/850 nm narrow bandwidth high power NIRLEDS used in iHD terminal..
90
Appendix D
BIDIRECTIONAL ASSOCIATIVE MEMORY (BAM)
We will first look at the derivation and the algorithm of BAM. Then we will present
the conditions for complete recall.
Given input data vector rPn,
target data vector rtn,
where r denotes the rth sample, and
n denotes the nth element,
first step is to generate BAM vector Wn×n
W =R∑
r=1
rqrtrpT (D.1)
It is followed by the following recurrent steps for a new input p,
rc(0) = rp
ra(k) = f (Wrc(k))
rc(k+1) = f (WT ra(k)) (D.2)
and finally when equilibrium is achieved,
rc(N) = rp
ra(N) = rt (D.3)
91
that happens when energy E is minimal.
E(rP, rt) = −raTWrc (D.4)
The equilibrium is ascertained when vectors c and a remain unchanged over iter-
ations.
Although Kosko’s BAM (rq = 1) would result in equilibrium often, Yeou-Fang
Wang et al [26] showed that complete recall is only possible when E is in its local
minimum.
For p and p1 are different by one Hamming Distance, a pair (lp, lt) can be recalled
iff
nl = ltT rtrpT lp− lt1T rtrpT lp1 (D.5)
∀lR∑
r=1
rqnl ≥ 0 (D.6)
92
