Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC

Sanjeev Tannirkulam Chandrasekaran; Sumukh Prashant Bhanushali; Imon Banerjee; Arindam Sanyal

doi:10.1109/JETCAS.2021.3128587

Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC

Sanjeev Tannirkulam Chandrasekaran, Sumukh Prashant Bhanushali, Imon Banerjee, Arindam Sanyal

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

This work presents a mixed-signal, reservoir-computing neural network (RC-NN) for at-home, real-time health monitoring using intelligent wearable device. The proposed technique is demonstrated on stress detection from electrocardiogram (ECG) signal, and heart diseases detection using a fusion artificial intelligence (AI) model that combines demographic and physiological information. The RC-NN uses a static, random reservoir layer with short-term memory to nonlinearly project input data to high-dimensional plane, and allow easy separation using linear AI model at the output layer. The RC-NN is designed in 65nm CMOS process, and detects stress and heart-diseases with mean accuracies of 92.8% and 86.8% respectively, while consuming 10.97nJ/inference and 2.57nJ/inference respectively.

Original language	English (US)
Pages (from-to)	829-839
Number of pages	11
Journal	IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume	11
Issue number	4
DOIs	https://doi.org/10.1109/JETCAS.2021.3128587
State	Published - Dec 1 2021
Externally published	Yes

Keywords

Machine learning
cardiac diseases prediction
data fusion and medical wearable
health monitoring
reservoir computing
stress detection

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/JETCAS.2021.3128587

Cite this

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title = "Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC",

abstract = "This work presents a mixed-signal, reservoir-computing neural network (RC-NN) for at-home, real-time health monitoring using intelligent wearable device. The proposed technique is demonstrated on stress detection from electrocardiogram (ECG) signal, and heart diseases detection using a fusion artificial intelligence (AI) model that combines demographic and physiological information. The RC-NN uses a static, random reservoir layer with short-term memory to nonlinearly project input data to high-dimensional plane, and allow easy separation using linear AI model at the output layer. The RC-NN is designed in 65nm CMOS process, and detects stress and heart-diseases with mean accuracies of 92.8% and 86.8% respectively, while consuming 10.97nJ/inference and 2.57nJ/inference respectively.",

keywords = "Machine learning, cardiac diseases prediction, data fusion and medical wearable, health monitoring, reservoir computing, stress detection",

author = "{Tannirkulam Chandrasekaran}, Sanjeev and {Prashant Bhanushali}, Sumukh and Imon Banerjee and Arindam Sanyal",

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AU - Prashant Bhanushali, Sumukh

AU - Banerjee, Imon

AU - Sanyal, Arindam

PY - 2021/12/1

Y1 - 2021/12/1

N2 - This work presents a mixed-signal, reservoir-computing neural network (RC-NN) for at-home, real-time health monitoring using intelligent wearable device. The proposed technique is demonstrated on stress detection from electrocardiogram (ECG) signal, and heart diseases detection using a fusion artificial intelligence (AI) model that combines demographic and physiological information. The RC-NN uses a static, random reservoir layer with short-term memory to nonlinearly project input data to high-dimensional plane, and allow easy separation using linear AI model at the output layer. The RC-NN is designed in 65nm CMOS process, and detects stress and heart-diseases with mean accuracies of 92.8% and 86.8% respectively, while consuming 10.97nJ/inference and 2.57nJ/inference respectively.

AB - This work presents a mixed-signal, reservoir-computing neural network (RC-NN) for at-home, real-time health monitoring using intelligent wearable device. The proposed technique is demonstrated on stress detection from electrocardiogram (ECG) signal, and heart diseases detection using a fusion artificial intelligence (AI) model that combines demographic and physiological information. The RC-NN uses a static, random reservoir layer with short-term memory to nonlinearly project input data to high-dimensional plane, and allow easy separation using linear AI model at the output layer. The RC-NN is designed in 65nm CMOS process, and detects stress and heart-diseases with mean accuracies of 92.8% and 86.8% respectively, while consuming 10.97nJ/inference and 2.57nJ/inference respectively.

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KW - cardiac diseases prediction

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Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC

Abstract

Keywords

ASJC Scopus subject areas

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