TY - GEN
T1 - Recurrent neural network circuit for automated detection of atrial fibrillation from raw ECG
AU - Sadasivuni, Sudarsan
AU - Chowdhury, Rahul
AU - Karnam, Vinay Elkoori Ghantala
AU - Banerjee, Imon
AU - Sanyal, Arindam
N1 - Publisher Copyright:
© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2021
Y1 - 2021
N2 - A recurrent neural network (RNN) is presented in this work for automatic detection of atrial fibrillation from raw ECG signals without any hand-crafted feature extraction. We designed a stacked long-short term memory (LSTM) network - a special RNN with capability of learning long-term temporal dependencies in the ECG signal. The RNN is digitally synthesized in 65nm CMOS process, and consumes 21.8nJ/inference at 1kHz operating frequency, while achieving state-of-the-art classification accuracy of 85.7% and f1-score of 0.82. The energy consumption of the proposed RNN is 8× lower than state-of-the-art integrated circuits for arrhythmia detection.
AB - A recurrent neural network (RNN) is presented in this work for automatic detection of atrial fibrillation from raw ECG signals without any hand-crafted feature extraction. We designed a stacked long-short term memory (LSTM) network - a special RNN with capability of learning long-term temporal dependencies in the ECG signal. The RNN is digitally synthesized in 65nm CMOS process, and consumes 21.8nJ/inference at 1kHz operating frequency, while achieving state-of-the-art classification accuracy of 85.7% and f1-score of 0.82. The energy consumption of the proposed RNN is 8× lower than state-of-the-art integrated circuits for arrhythmia detection.
KW - Atrial fibrillation
KW - Electro-cardiograph
KW - Health monitoring
KW - Long-short term memory
KW - Recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85109044103&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85109044103&partnerID=8YFLogxK
U2 - 10.1109/ISCAS51556.2021.9401666
DO - 10.1109/ISCAS51556.2021.9401666
M3 - Conference contribution
AN - SCOPUS:85109044103
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Y2 - 22 May 2021 through 28 May 2021
ER -