標題: A 48.6-to-105.2 mu W Machine Learning Assisted Cardiac Sensor SoC for Mobile Healthcare Applications
作者: Hsu, Shu-Yu
Ho, Yingchieh
Chang, Po-Yao
Su, Chauchin
Lee, Chen-Yi
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: Arrhythmia;biomedical signal processor;classification;ECG;feature extraction;machine learning;myocardial infarction;VCG.
公開日期: 1-四月-2014
摘要: 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
http://hdl.handle.net/11536/24263
ISSN: 0018-9200
DOI: 10.1109/JSSC.2013.2297406
期刊: IEEE JOURNAL OF SOLID-STATE CIRCUITS
Volume: 49
Issue: 4
起始頁: 801
結束頁: 811
顯示於類別:期刊論文


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