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Detection of hypoglycemic events through wearable sensors

Detection of hypoglycemic events through wearable sensors

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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.

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Detection of hypoglycemic events through wearable sensors

  • 1. Detection of Hypoglycemic Events through Wearable Sensors Jean-Eudes Ranvier - Fabien Dubosson - Jean-Paul Calbimonte - Karl Aberer SEMPER, ESWC. May 2016.
  • 2. 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
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. Data acquisition • ECG (250 Hz) • Breathing signal (18 Hz) • Accelerometers (100 Hz) • Signals are noisy, acquired in real-life conditions. ECG Breathing Acc.
  • 7. 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
  • 8. Modeling Physiological 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
  • 9. Feature extraction Preprocessing (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
  • 10. 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) l acquisition and feature extraction Physiological symptoms model Baseline Hypoglycemic hyperinsulemia Time (s) ECG(mV) P R S T Q Energy intake Energy expenditure model Activity recognition Electrocardiogram Accelerometers Breathing
  • 11. Glycemic Events Detection Preprocessing (Breathing) • Breathing rate • White noise removal • Simple low pass filter • Categorization of breathing rate • Used to model activity level 11
  • 12. 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)
  • 13. 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 data SELECT ?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)} ) } }
  • 14. 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 approach Example: glucose lower than 4 in the last day and/or systolic bigger than 140 and diastolic bigger than 90. 14
  • 15. Complex Events Example: glucose lower than 4 in the last day.
  • 16. 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
  • 17. 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.
  • 18. 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
  • 19. 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 Recall Hypoglycemic 0.78 0.68 Normal 0.93 0.96 Total 0.91 0.91 Accuracy 0.91
  • 20. 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
  • 21. Detection of Hypoglycemic Events through Wearable Sensors Jean-Eudes Ranvier - Fabien Dubosson - Jean-Paul Calbimonte - Karl Aberer Questions?

Editor's Notes

  1. MENTION CGM
  2. videlicet
  3. Order of the signals raw signal (black) Filter signal ( blue) Result of the mathematical morphology operator
  4. Carbs, fatty acid and proteins all give energy, by not at the same rate, nor at the same time
  5. The fact that the accuracy is the same than precision and recall is a coincidence