MENTAL STRESS LEVEL MEASUREMENT USING HRV

ANALYSIS

Presented byL. VanithaReg.No: 1224499804Research Scholar (Part Time)

SupervisorDr.G.R.SureshProfessor/ECEEaswari Engineering College,Ramapuram.

OBJECTIVE The main objective of this work is to design an

efficient Mental Stress Level measuring system using HRV time domain and frequency domain parameters extracted from ECG signals.

The efficient system is built by designing an efficient classifier combination scheme

The database for this work is taken from physionet database

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Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

References

CONTENTS

Introduction

Literature Survey

Proposed Method

Conferences Presented

Journal Submitted

Conclusion

References

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INTRODUCTION Mental stress

feeling of strain and pressure perceive things as threatening do not believe that their resources for coping with the

circumstances demand the demands being placed on us exceed our ability to cope body’s reaction to a change requires a physical, mental response.

Reasons financial worries family circumstances job

Stress leads to Physical illnesses

- heart attacks, arthritis, and chronic headaches Psychological diseases like:

- mental illness, anger, anxiety, and depression 4

Introduction

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Proposed Method

Conferences presented

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References

EFFECT OF STRESS

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ORGAN EFFECT

Eye Dilates the Pupil

Heart Increases rate and force of contraction

Lungs Dilates bronchioles

Blood Vessels Constricts

Sweet glands Activates sweet secretion

Kidney Increases rennin secretion

Brain Secretes Adrenaline

Skeletal Muscle

Tighten

Pancreas Inhibits insulin secretion

STRESS MEASURING METHODS Psychological Method

conducting interviews or filling questionnaires

Behavioural Method the manner and rhythm in which a person types characters on a

keyboard or keypad based on facial recognition

Physical Method Biochemical response involving changes in the endocrine and

immune systems Physiological response indicative of central-autonomic activity

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Introduction

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Conferences presented

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PHYSIOLOGICAL METHODS Common physiological signals - used to detect mental stress

in human beings are Blood Volume Pulse (BVP) Galvanic Skin Response (GSR) Electrocardiogram (ECG) Pupil Dimension (PD) Skin temperature (ST) Electroencephalogram (EEG) Finger Temperature (FT) Electromyography (EMG)

Advantages signals are acquired in a non-intrusive manner Not possible of faking the effect

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Introduction

Literature Survey

Proposed Method

Conferences presented

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Conclusion

References

LITERATURE SURVEY Year : 2012

Author : F. Alamudun, J.Choi, R. Gutierrez-Osuna, H,KhanTitle : Removal of Subject-dependent and Activity-Dependent Variation in Physiological Measures of Stress Features : HRV Classifier: Fisher's Linear Discriminant analysis Experimental Condition: Sitting, standing, slow walking and fast walking Output State : 2 states No. Of Subjects: 14 Classification Efficiency: 82 Advantages:

Simple experiment set up Drawbacks :

Less efficiency

F. Alamudun, J.Choi, R. Gutierrez-Osuna, H,Khan and B.Ahmed, “Removal of Subject-dependent and Activity-Dependent Variation in Physiological Measures of Stress”, 6th Internationatinal Conference on Pervasive Computing Technologies for Healthcare, 2012, pp. 115-122

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Introduction

Literature Survey

Proposed Method

Conferences presented

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Conclusion

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LITERATURE SURVEY Year : 2012

Author : Hariton Costin, Cristian Rotariu, Alexandru PasaricaTitle : Mental Stress Detection Using Heart Rate Variability and Morphologic Variability of ECG Signals Features : HRV, Morphological Variability Classifier: ANOVA Experimental Condition: Driving – Physionet database Output State : Low, Medium, High No. Of Subjects: 16 Classification Efficiency: 90 % Advantages:

High Classification Efficiency Drawbacks :

Efficiency can be improved

Hariton Costin, Cristian Rotariu, Alexandru Pasarica, “Mental Stress Detection Using Heart Rate Variability and Morphologic Variability of ECG Signals, “International Conference and Exposition on Electrical and Power Engineering, Romania, October 2012, pp. 591-596.

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Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

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LITERATURE SURVEY Year : 2013

Author : Wijsman, J.L.P. and Vullers, R. and Polito, S. and Agell, Title : Towards ambulatory mental stress measurement from physiological parameters Features : ECG, EMG, Respiration, Skin conductance Classifier: ANOVA Experimental Condition: Stroop test Output State : 2 state No. Of Subjects: 4 Classification Efficiency: 72 % Advantages:

Simple experiment set up Drawbacks :

Less efficiency Less number of subjects

Wijsman, J.L.P. and Vullers, R. and Polito, S. and Agell, C. and Penders, J. (2013) Towards ambulatory mental stress measurement from physiological parameters, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 2-5 Sept 2013, Geneva, Switzerland. pp. 564-569. IEEE Computer Society

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Introduction

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Conferences presented

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LITERATURE SURVEY Year : 2013

Author : Wijsman.J., Grundlehner.B., Hao Liu, Penders.JTitle : Wearable Physiological Sensors Reflect Mental Stress State in Office-Like Situations Features : ECG, EMG, Respiration, Skin conductance Classifier: ANOVA Experimental Condition: Stroop test Output State : 2 state No. Of Subjects: 6 Classification Efficiency: 74.5 % Advantages:

Conducted in real time Drawbacks :

Low efficiency

Wijsman.J., Grundlehner.B., Hao Liu, Penders.J., “Wearable Physiological Sensors Reflect Mental Stress State in Office-Like Situations”, IEEE Affective Computing and Intelligent Interaction, 2013, pp. 600-605.

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Introduction

Literature Survey

Proposed Method

Conferences presented

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Conclusion

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RESULTS SURVEY

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Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

References

RESULTS SURVEY GA - Genetic Algorithm ENN - Elman Neural Network LR - Logistic Regression PDM - Principal Dynamic Modes FSVM - Fuzzy Support Vector Machine DSS -Decision Support System SVM - L - SVM Linear SVM - S - SVM - Sigmoidal KNN - KNN classifier SVM-NCC - SVM Nearest Class Center algorithm HRV – Heart Rate Variability ST – Skin Temperature BVP – Blood Volume Pulse MV – Morphological Variability GSR – Galvanic Skin Response PD – Pupil Diameter BVP – Blood Volume Pulse PPG – Photoplethysmogram RR – Respiration Rate EMG – Electromyography BR - Breathing mvmt - Movement SC – Skin Conductance EDA – Electrodermal activity 13

Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

References

PROPOSED METHOD

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Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

References

Input ECG Signals (Test

Pattern)

HRV Determinatio

n Feature Extraction

Classification

Output Stress Level

Input ECG Signals

(Training Pattern)

Feature Extraction

Learning

INPUT ECG SIGNALS ECG signal Acquisition

Stress during driving conditionThe driving is planned in such a way that in an approximately 1 hour drive, the subject undergoes the different stress conditions which are: rest before driving (no stress), low stress, medium stress and high stress.

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Introduction

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HRV & FEATURE EXTRACTION A measure of neurocardiac function that reflects heart-

brain interactions and autonomic nervous system dynamics

From ECG signal RR interval is determined RR interval is also called as HRV – Heart rate variability HRV is the beat-to-beat variation in heart rate

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Introduction

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Conferences presented

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Conclusion

References

HRV PARAMETERS

TIME & FREQUENCY DOMAIN PARAMETERS

mRR – mean RR interval mHR – mean Heart rate Very low frequency (VLF) – 0 - 0.04 Hz Low frequency (LF) - 0.04 – 0.15 Hz High frequency (HF) – 0.15 – 0.4 Hz Normalized very low frequency (nVLF) Normalized low frequency (nVLF) Normalized high frequency spectrum (dLFHF) Symphatovagal balance index (SVI)

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Introduction

Literature Survey

Proposed Method

Conferences presented

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Conclusion

References

FORMULAE

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Introduction

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Proposed Method

Conferences presented

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CLASSIFICATION Based on the Literature Survey 3 types of

classification is performed

2 type classification – Stress, No Stress 4 type classification – No stress, Low stress,

Medium Stress, High Stress Stress on a 10 point scale

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Introduction

Literature Survey

Proposed Method

Conferences presented

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CLASSIFICATION Based on Literature Survey different Classification

Algorithms used for stress measurement Logistic Regression Linear Discriminant Analysis Fisher Discriminant Analysis Bayes Classifier ANOVA Analysis KNN Fuzzy logic Neural Network Support Vector Machine

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CLASSIFICATION PROCEDURE Identify the classes of patterns

Assessment of pattern structure Assessment of probabilistic character

Determine the features Determine the constraints on system performance Identify training and testing data Identify the suitable classification algorithm Train the system Iterate until the desired performance is achieved Test the system Determine the system performance

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Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

References

CLASSIFICATION METHODS

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Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

References

Method PropertyTemplate Matching Assigns patterns to the most similar

template

Nearest Mean Classifier Assigns patterns to the nearest class mean

Subspace Method Assign patterns to the nearest class subspace

1-Nearest Neighbor Rule Assign patterns to the class of the nearest training pattern

K- Nearest Neighbor Rule Assign patterns to the majority class among k nearest neighbor

Fisher Linear Discriminant

Linear Classifier using MSE optimization

Perceptron Iterative Optimization of a linear classifier

Binary Decision Tree Find a set of thresholds for a pattern-dependent sequence of features

Multi-Layer Perceptron Iterative MSE optimization of two or more layers of perceptron

Support Vector Machine Assign patterns to the class of the nearest training pattern

CLASSIFIER COMBINATION Main objective is to improve the overall classification

accuracy. Classifier Combination method derives its decision by

combining the individual decision of multiple classifiers Classifier Problem – two phase

Decide classifiers Combination function - combines the results of the

individual classifiers to make the final decision

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Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

References

CLASSIFIER COMBINATIONReasons for combining multiple classifiersDifferent feature setsDifferent training sessionsDifferent classifiers trained on the same data - differ in their global performances and local differences. Some classifiers such as neural networks show different results with different initializations due to the randomness inherent in the training procedure.

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Introduction

Literature Survey

Proposed Method

Conferences presented

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Conclusion

References

COMBINATION SCHEMES

Various schemes for combining multiple classifiers based on architecture Parallel Cascading (or serial combination) Hierarchical (tree-like)

Using these three basic architectures, more complicated classifier combination systems can be built

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Introduction

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Proposed Method

Conferences presented

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Conclusion

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INPUT ECG SIGNAL – DRIVER 1

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Introduction

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Driver 1 (Minutes)

Mean RR

SDNN RMSSD NN50 pNN50 VLF LF HF LF/HF

0-5 355.52 31.475 29.88 22 2.6128 15.27 17.294 67.2870.2570

1

6-10 450.06 42.538 36.376 47 4.075 13.322 15.994 60.4360.2646

4

11-15 569.43 54.03 38.25 45 5.714 17.072 21.415 61.0350.3508

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16-20 395.15 51.326 60.509 39 5.1451 19.802 30.368 49.54 0.61299

21-25 301.71 39.046 47.599 46 4.6324 33.773 25.077 40.848 0.6139

26-30 301.71 39.046 47.599 46 4.6324 33.773 25.077 40.848 0.6139

31-35 314.24 61.858 37.266 51 5.3459 73.134 11.168 15.626 0.71469

36-40 291.51 66.839 87.26 33 5.665 17.958 34.186 47.565 0.71872

41-45 381.39 52.462 50.987 52 6.6158 44.382 23.225 32.198 0.72133

46-50 396.82 49.272 61.343 36 4.7682 24.711 33.832 41.167 0.82181

50-55 396.82 49.272 61.343 36 4.7682 24.711 33.832 41.167 0.82181

55-60 396.82 49.272 61.343 36 4.7682 24.711 33.832 41.167 0.82181

HRV & FEATURE EXTRACTION

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Introduction

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Proposed Method

Conferences presented

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Conclusion

References

CLASSIFICATION – CASCADE

Two stage classification Self Organizing Map Support vector machine

Stress levels No stress Low stress Medium stress High stress

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Introduction

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Conferences presented

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Conclusion

References

• The above work is presented in IEEE Conference L.Vanitha, G.R. Suresh, “Hybrid SVM Classification Technique to Detect Mental Stress in

Human Beings Using ECG Signals”, 2013 International Conference on Advanced Computing and Communication Systems (ICACCS -2013), Dec. 19 – 21, 2013, Sri Eshwar college of Engineering, Coimbatore

BLOCK DIAGRAM OF HYBRID CLASSIFIER FOR STRESS MEASURING SYSTEM

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Introduction

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Proposed Method

Conferences presented

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Conclusion

References

CLASSIFICATION – HIERARCHICAL

Three stage Hierarchical classification Support vector machine

Stress levels No stress Low stress Medium stress High stress

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Introduction

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Proposed Method

Conferences presented

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Conclusion

References

• The above work is presented in IEEE Conference

L.Vanitha, G.R. Suresh, “Hierarchical SVM to detect Mental Stress in Human Beings using Heart Rate Variability”, 2nd International Conference on Devices Circuits and systems (ICDCS’14), 6th - 8th March 2014, Karunya University, Coimbatore

HIERARCHICAL SVM TO DETECT MENTAL STRESS IN HUMAN BEINGS USING HEART RATE VARIABILITY

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Introduction

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Proposed Method

Conferences presented

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References

CLASSIFICATION - PARALLEL

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Introduction

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Conferences presented

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References

COMBINATION SCHEMES Objective is to find a combining rule for an improved

estimation of the final posterior probability, P(Yi|xt), based on the individual Pj(Yi|xt) from each classifier hj

Training dataset Dtr with m instances, represented as {xq,yq}, q=1,...,m,

where xq is an instance in the n-dimensional feature space X,

yq ∈Y={1,...,C} is the class identity label associated with xq

Through a training procedure L classifiers is developed hj, j =1,...,L.

Therefore, for each testing instance xt in the testing dataset Dte, each classifier can vote an estimate of the posterior probability across all the possible class labels Pj(Yi|xt), j =1,...,L and Yi =1,...,C

Pj(Yi|xt), where j =1,...,L and Yi =1,...,C, the testing instance xt is assigned to Yi provided that the posterior probability is maximum

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Introduction

Literature Survey

Proposed Method

Conferences presented

Journal Submitted

Conclusion

References

COMBINATION SCHEMES

Geometric average rule

Arithmetic average rule

Majority voting rule

Median value rule34

Introduction

Literature Survey

Proposed Method

Conferences presented

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Conclusion

References

COMBINATION SCHEMES Borda count rule

Max

Min rule

Weighted average rule

Weighted majority voting rule

wij – weight coefficient for classifier

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Introduction

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Conferences presented

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PERFORMANCE EVALUATION

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Introduction

Literature Survey

Proposed Method

Conferences presented

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Conclusion

References

PERFORMANCE EVALUATION

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Introduction

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Conferences presented

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Conclusion

References

CONFERENCES PRESENTED L.Vanitha, G.R. Suresh, “ Performance Analysis on Physiological Signals in

Mental Stress Level Measurement”, IISAT’13 IEEE International Conference in Intelligent Interactive Systems and Assistive Technologies, Jan 2nd -3rd 2013, Kumaraguru college of technology, Coimbatore

L.Vanitha, G.R. Suresh, “Hybrid SVM Classification Technique to Detect Mental Stress in Human Beings Using ECG Signals”, 2013 International Conference on Advanced Computing and Communication Systems (ICACCS -2013), Dec. 19 – 21, 2013, Sri Eshwar college of Engineering, Coimbatore

L.Vanitha, G.R. Suresh, “Sudden Cardiac Death Prediction System Using Hybrid Classifier”, International Conference on Electronics and Communication Systems (ICECS2014), 13-14th February 2014, Karpagam college of engineering, Coimbatore

 L.Vanitha, G.R. Suresh, “Hierarchical SVM to detect Mental Stress in Human Beings using Heart Rate Variability”, 2nd International Conference on Devices Circuits and systems (ICDCS’14), 6th - 8th March 2014, Karunya University, Coimbatore

L.Vanitha, G.R. Suresh, “ Efficient Hybrid Classifier to Predict Cardiac Arrest”, 4th Internatinal Conference on Recent Trends in Information Technology (ICRTIT 2014), 10th – 12th April, Anna University, Chennai 38

Introduction

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JOURNAL SUBMITTED L.Vanitha, G.R. Suresh, “Mental Stress Level Detection System Using

Classifier Combination Technique ”, International Journal of Communication and Networking Technologies.

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CONCLUSION

Determining the stress level is very important, as it causes many health related problems.

HRV determined from ECG is a reliable measure to detect stress level

Combination of Classifiers improves the efficiency to determine the Mental Stress Level.

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References

REFERENCES Jongyoon Choi, Beena Ahmed, and Ricardo Gutierrez-Osuna, “Development and Evaluation of an

Ambulatory Stress Monitor Based on Wearable Sensors”, IEEE Transactions on Information Technology in Biomedicine, Vol. 16, No. 2, March 2012, pp. 279-286

Hariton Costin, Cristian Rotariu, Alexandru Pasaric, “Mental Stress Detection using Heart Rate Variability and Morphologic Variability of ECG Signals “,”, International Conference and Exposition on Electrical and Power Engineering, 25-27 Oct. 2012, pp. 591 – 596

Jeen-Shing Wang, Che-Wei Lin, and Ya-Ting C. Yang, “Using Heart Rate Variability Parameter-Based Feature Transformation Algorithm for Driving Stress Recognition”, In proceeding of: Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011, pp. 532-537.

F. Mokhayeri, M-R. Akbarzadeh-T, S. Toosizadeh, “Mental Stress Detection Using Physiological Signals Based on Soft Computing Techniques”, 18th Iranian Conference on BioMedical Engineering, 14-16 December 2011, Tehran, Iran, IEEE, pp. 232-237.

Jacqueline Wijsman, Bernard Grundlehner, Hao Liu, Hermie Hermens, and Julien Penders, “Towards Mental Stress Detection Using Wearable Physiological Sensors”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2011), IEEE, pp. 1798-1801.

Cornelia Setz, Bert Arnrich, Johannes Schumm, Roberto La Marca, "Discriminating Stress From Cognitive Load Using a Wearable EDA Device", IEEE Transactions on Information Technology in Biomedicine, Vol 14, No. 2 March 2010

Mohammad Ali Khalilzadeh, Seyyed Mehran Homam, Seyyed Abed Hosseini, Vahid Niazmand, “Qualitative and quantitative evaluation of brain activity in emotional stress”, Iranian Journal of Neurology, Vol.8, No.28, 2010, pp. 605-618.

Cornelia Setz, Bert Arnrich, Johannes Schumm, Roberto La Marca, “Discriminating Stress from Cognitive Load Using a Wearable EDA Device”, IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 2, March 2010. 41

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REFERENCESDoctorsDr. K. Murali, Psychiatrist, PonamalleeDr. R. Ponnudurai, Psychiatrist, ChennaiDr. Christina Augustine, Psychologist, ChennaiDr. M. MuraliKrishnan, Physiologist, ChengalpattuDr. S. Gomathi, Physiologist, MMC, ChennaiDr. R. Sivakumar, Cardiologist, MMM HospitalDr. S. Rajan, Cardiologist, MMM HospitalDr. G.P. Youvaraj, Professor, Advanced Study in Mathematics, Madras UniversityDr. A. Winslin, Professor, Mathematics

HospitalsKilpauk Medical College Hospital, ChennaiVidya Mental Health and Educational Trust, ChennaiVazhikatti Mental Health Centre and Research Institute, Coimbatore

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