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

被引：0

作者：

Bhanushali, Sumukh Prashant ^{[1
]}

Sadasivuni, Sudarsan ^{[1
]}

Banerjee, Imon ^{[2
]}

Sanyal, Arindam ^{[1
]}

机构：

[1] Univ Buffalo, Elect Engn Dept, Buffalo, NY 14260 USA

[2] Emory Univ, Dept Biomed Informat, Atlanta, GA 30322 USA

来源：

2020 IEEE 63RD INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS) | 2020年

关键词：

D O I：

10.1109/mwscas48704.2020.9184466

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：978 / 981

页数：4

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