Multi-Task Learning Mixed-Signal Classifier for In-situ Detection of Atrial Fibrillation and Sepsis

Sudarsan Sadasivuni, Sumukh Prashant Bhanushali, Sai Srinivasa Singamsetti, Imon Banerjee, Arindam Sanyal

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

Abstract

This paper presents an on-chip analog machine learning (ML) classifier IC for detecting atrial fibrillation (AFib) and sepsis from electrocardiogram (ECG) signal. The proposed technique allows continuous in-situ health surveillance using wearables with embedded AI for early detection of underlying health issues. The analog classifier uses custom activation function and performs in-memory computation (IMC) with switched-capacitor circuits for reduced data movement. Designed in 65nm, the test chip achieves average accuracy of 98.2% for AFib detection, and 90.7% for predicting sepsis 4 hours before onset. The energy efficiency of the test-chip is 12.9nJ/classification which is 4× better than state-of-the-art.

Original languageEnglish (US)
Title of host publicationBioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728172040
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 - Virtual, Online, Germany
Duration: Oct 6 2021Oct 9 2021

Publication series

NameBioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings

Conference

Conference2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021
Country/TerritoryGermany
CityVirtual, Online
Period10/6/2110/9/21

Keywords

  • atrial fibrillation
  • Machine learning
  • mixed-signal classifier and in-memory computation
  • sepsis

ASJC Scopus subject areas

  • Hardware and Architecture
  • Biomedical Engineering
  • Electrical and Electronic Engineering

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