Recurrent neural network circuit for automated detection of atrial fibrillation from raw ECG

Sudarsan Sadasivuni, Rahul Chowdhury, Vinay Elkoori Ghantala Karnam, Imon Banerjee, Arindam Sanyal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728192017
DOIs
StatePublished - 2021
Externally publishedYes
Event53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Daegu, Korea, Republic of
Duration: May 22 2021May 28 2021

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2021-May
ISSN (Print)0271-4310

Conference

Conference53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Country/TerritoryKorea, Republic of
CityDaegu
Period5/22/215/28/21

Keywords

  • Atrial fibrillation
  • Electro-cardiograph
  • Health monitoring
  • Long-short term memory
  • Recurrent neural network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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