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

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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

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Bioharness

ML

CEP

Alerts/Notifications

SEM

Data acquisition

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• 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

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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

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Original

Processed

QRS segmentation & annotation

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• 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

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Nutriments intake

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• 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.

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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

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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

Questions?