Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor

This paper presents a digital machine learning circuit for classifying stress condition from chest ECG signal from a wearable sensor. To minimize hardware cost, we use only 5 time-domain features that have much lower area and power consumption than frequency domain or combination of time and frequen...

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Bibliographic Details
Published in2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS) pp. 978 - 981
Main Authors Prashant Bhanushali, Sumukh, Sadasivuni, Sudarsan, Banerjee, Imon, Sanyal, Arindam
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2020
Subjects
Biomedical monitoring
Electrocardiography
Feature extraction
Frequency-domain analysis
Radio frequency
Stress
Time-domain analysis
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Summary:This paper presents a digital machine learning circuit for classifying stress condition from chest ECG signal from a wearable sensor. To minimize hardware cost, we use only 5 time-domain features that have much lower area and power consumption than frequency domain or combination of time and frequency domain features as is used conventionally. We test the time-domain features on several machine learning algorithms. Random Forest classifier shows the best classification accuracy of 0.96 with the time-domain features at an estimated power consumption of only 1.16mW at 65nm CMOS process which demonstrates feasibility of embedding a machine learning classifier in a wearable ECG sensor for real-time, continuous stress detection.
ISSN:1558-3899
DOI:10.1109/MWSCAS48704.2020.9184466