This document proposes a non-invasive method to detect hypoglycemic events in patients with type 1 diabetes using wearable sensors. The method involves collecting physiological and activity data from sensors like ECG, accelerometers and breathing monitors. Machine learning models analyze the data to detect glycemic events, which are then represented semantically and used to generate alerts. Preliminary tests show the physiological model can accurately detect hypoglycemic events based on continuous glucose monitor data. The system aims to help patients and practitioners monitor insulin levels without invasive blood glucose testing.

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?