TY - JOUR
T1 - Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC
AU - Tannirkulam Chandrasekaran, Sanjeev
AU - Prashant Bhanushali, Sumukh
AU - Banerjee, Imon
AU - Sanyal, Arindam
N1 - Publisher Copyright:
© 2011 IEEE.
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.
KW - Machine learning
KW - cardiac diseases prediction
KW - data fusion and medical wearable
KW - health monitoring
KW - reservoir computing
KW - stress detection
UR - http://www.scopus.com/inward/record.url?scp=85121828196&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121828196&partnerID=8YFLogxK
U2 - 10.1109/JETCAS.2021.3128587
DO - 10.1109/JETCAS.2021.3128587
M3 - Article
AN - SCOPUS:85121828196
SN - 2156-3357
VL - 11
SP - 829
EP - 839
JO - IEEE Journal on Emerging and Selected Topics in Circuits and Systems
JF - IEEE Journal on Emerging and Selected Topics in Circuits and Systems
IS - 4
ER -