Title: A 48.6-to-105.2 mu W Machine Learning Assisted Cardiac Sensor SoC for Mobile Healthcare Applications
Authors: Hsu, Shu-Yu
Ho, Yingchieh
Chang, Po-Yao
Su, Chauchin
Lee, Chen-Yi
Department of Electronics Engineering and Institute of Electronics
Keywords: Arrhythmia;biomedical signal processor;classification;ECG;feature extraction;machine learning;myocardial infarction;VCG.
Issue Date: 1-Apr-2014
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 mu W for real- time syndrome detections of ECG- based arrhythmia/ VCG- based myocardial infarction with 95.8/ 99% detection accuracy, respectively.
URI: http://dx.doi.org/10.1109/JSSC.2013.2297406
ISSN: 0018-9200
DOI: 10.1109/JSSC.2013.2297406
Volume: 49
Issue: 4
Begin Page: 801
End Page: 811
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