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Detection of Hypoglycemic Events through Wearable
Sensors
Jean-Eudes Ranvier - Fabien Dubosson - Jean-Paul Calbimonte - Karl Aberer
SEMPER, ESWC. May 2016.
Context• Diabetes type-1 disease: insulin deficiency
• Up to 10 hypoglycemia episodes per week
• 4% of DT1 deaths caused by hypoglycemic attacks
• Treatment: insulin shots, insulin levels under control
• Monitor insulin levels:• Drop of blood • Continuous glucose monitoring
• Need for Non-Invasive monitoring
Overview• DT1 personal-sensing application
• Monitor patient activity• Detection of hypoglycemic events• Semantic enhancement of process
data• Reasoning on glycemic events• Feedback to patient / practitioner
• Diabetes type-1 patients• Non invasive sensors• Off-the-shelf bioharness• Leverage body signals and energy
expenditure
3
Estimation of glucose level based on physiological model and energy expenditure model + alert system
Architecture• Android Mobile App• Data collected from Bioharness
via Bluetooth• Centralized data processing
• Machine Learning for event detection
• Semantic interpretation of the signals
• Complex event processing• Alerts and Notifications back to
the smartphone• Web based visualization
interface
4
Bioharness
ML
CEP
Alerts/Notifications
SEM
Data acquisition
5
• Requires ~20 participants • Collaboration with an hospital• Min. 4 consecutive days for 12 hours• Continuous glucose monitoring (1 sample / 5
minutes)• Sensor belt (ECG, accelerometer, breathing)• Food intake• Activities annotations
Data acquisition• ECG (250 Hz)• Breathing signal (18 Hz)• Accelerometers (100 Hz)• Signals are noisy, acquired in real-life conditions.
ECG Breathing Acc.
Glycemic Events Detection• Off-the-shelf sensor
• ECG• Breathing Rate• Accelerometer
• Processing pipeline• Signal pre-processing• Raw data -> meaningful data• Features extraction• Modeling
• Physiological• Energy Expenditure
7
ModelingPhysiological Energy expenditure
1 minute sliding window agg. Need for convolution to account for intake delay*
• Q,R,S,T amplitudes• ST Fourier transform components• QTc interval• HR
• Glucose, carbohydrates• activity level (VMU)• HR ( by product of HB proc.)• Breathing rate
Feature extractionPreprocessing (ECG)
• Adaptive filter (NLMS)
• Linear filter + adaption of the weights vector
• Remove correlated artifacts based on noise signals (viz. accelerometers & breathing)
• Linear time complexity• Incremental• Approximate Entropy
9
Original
Processed
QRS segmentation & annotation
10
• Based on mathematical morphology (Yadzani et al.)
• Detection of QRS complexes
• Double advantage• Analysis of HB shape
• Accurate detection of HR
• Linear time and incremental
• Detected segments are assigned fiducial points labels (PQRST)
Feature extraction (ECG)
Glycemic Events DetectionPreprocessing (Breathing)
• Breathing rate• White noise removal• Simple low pass filter• Categorization of breathing rate• Used to model activity level
11
Nutriments intake
12
• Use Fitbit API
• Estimate of calories / glucose
• Useful essentially for the activity part
• Requires user manual input
• Gives different semantic meaning to different energy forms
• Rely on 3rd party API
Feature extraction (intake)
Semantic representation
hypo1 a :HypoEvent; :observedAt "2016-03-03T20:30:31"; :hasValue 45.3.syst1 a :SystolicObs; :observedAt "2016-03-03T20:30:31"; :hasValue 145.
• E.g. Hypo/Hyperglicemic events
• Physical activities
• Food intake
• Live Queries over the streaming dataSELECT ?h1,?sys FROM NAMED WINDOW :win ON ex:eventStream [RANGE 1h]WHERE { WINDOW :win { SEQ({?h1 a :HypoEvent}, {?h2 a :SystolicObs; :hasValue ?sys. FILTER (?sys>140)} ) }}
Dynamic rule editor• Motivation: doctors need to query the data or even
personalize the monitoring rules to the patient.
• Definition of Graphically Programmable Rules
• Web-based rule editor relying on the event calculus
• The idea is to handle:• Composite events (discrete & continuous)• Sequential events (discrete & continuous)• Combination of the above
• Declarative approachExample: glucose lower than 4 in the last day and/or systolic bigger than 140 and diastolic bigger than 90.
14
Complex Events
Example: glucose lower than 4 in the last day.
Combination and Sequence of Complex Events
Example: glucose lower than 4 in the last day and/or systolic bigger than 140 and diastolic bigger than 90.
Example: glucose lower than 4 followed by a glucose bigger than 8
GUI to personalize the rules
• Reasoner is based on an indexed version of the the Event Calculus.
• Graphical Editor is based on JavaScript.• Rules are encoded in JSON and parsed to Prolog/Event Calculus.• SOA architecture: reasoner embedded in a Web Service.
Tests & Validation• Accuracy of the models: re-use the dataset collected
• Performance evaluation by cross-validation of existing data
• Usability test on non diabetic participants• Quantitative analysis of the ease of use of the platform
• Qualitative evaluation of the platform by medical staff• Qualitative review of the platform by the medical staff
18
Preliminary model evaluation
• 13 hypoglycemic events
• Physiological model only, CGMS as ground truth
• Random forest of 100 trees• Accuracy biased due to class
imbalance
Precision RecallHypoglycemi
c0.78 0.68
Normal 0.93 0.96Total 0.91 0.91
Accuracy 0.91
Conclusion• Innovative and non-invasive way of
detection hypo events• leveraging off-the-shelf sensors• Intercommunication sensor-phone-servers• heaving computation computed server side• Promising results• Work in progress• Potential need for tailored models• Privacy concerns to address
Detection of Hypoglycemic Events through Wearable Sensors
Jean-Eudes Ranvier - Fabien Dubosson - Jean-Paul Calbimonte - Karl Aberer
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