A 2.66 µW Clinician-Like Cardiac Arrhythmia Watchdog Based on P-QRS-T for Wearable Applications

• Published in: IEEE transactions on biomedical circuits and systems Vol. 16; no. 5; pp. 793 - 806
• Main Authors: Xu, Xinzi, Cai, Qiao, Zhao, Yang, Wang, Guoxing, Zhao, Liebin, Lian, Yong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Algorithms; Arrhythmia; Arrhythmias, Cardiac - diagnosis; Biological neural networks; Cardiovascular diseases; Clinician-like cardiac arrhythmia classification; Databases, Factual; Detection algorithms; ECG; EKG; Electrocardiography; Energy efficiency; Feature extraction; Heart beat; Heart rate; Humans; P and T waves detection; P waves; QRS detection; Signal Processing, Computer-Assisted; wearable ECG sensors; Wearable Electronic Devices; Wearable sensors; Wearable technology
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2022.3184971

A wearable electrocardiogram (ECG) device is an effective tool for managing cardiovascular diseases. This paper presents a low power clinician-like cardiac arrhythmia watchdog (CAW) for wearable ECG devices. The CAW is based on a novel P-QRS-T detection algorithm that makes use of clinical features to identify abnormalities. Implemented in 0.18 μm CMOS process, the CAW consumes 2.66 µW for 80 bpm heart rate at 1.2 V supply with an area of 0.578 mm 2 . Verified on QT database, the average sensitivity/positive predictivity for P-wave, QRS complex and T-wave are over 93.39%/88.55%, 99.69%/99.48%, and 97.13%/93.18% respectively, across over 190000 beats. It shows over 99.8% arrhythmia detection accuracy for 43 subjects evaluated on MIT-BIH database.