A 48.6-to-105.2 μw machine learning assisted cardiac sensor SoC for mobile healthcare applications

Shu Yu Hsu, Yingchieh Ho, Po Yao Chang, Chau-Chin Su, Chen-Yi Lee

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

A machine-learning (ML) assisted cardiac sensor SoC (CS-SoC) is designed for mobile healthcare applications. The heterogeneous architecture realizes the cardiac signal acquisition, filtering with versatile feature extractions and classifications, and enables the higher order analysis over traditional DSPs. Besides, the asynchronous architecture with dynamic standby controller further suppresses the system active duty and the leakage power dissipation. The proposed chip is fabricated in a 90-nm standard CMOS technology and operates at 0.5 V-1.0 V (0.7 V-1.0 V for SRAM and I/O interface). Examined with healthcare monitoring applications, the CS-SoC dissipates 48.6/105.2 μW for real-time syndrome detections of ECG-based arrhythmia/VCG-based myocardial infarction with 95.8/99% detection accuracy, respectively.

Original languageEnglish
Article number6712138
Pages (from-to)801-811
Number of pages11
JournalIEEE Journal of Solid-State Circuits
Volume49
Issue number4
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Arrhythmia
  • ECG
  • VCG
  • biomedical signal processor
  • classification
  • feature extraction
  • machine learning
  • myocardial infarction

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