Back to Search Start Over

An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection

Authors :
Jiawen Deng
Jieru Ma
Jie Yang
Shuyu Liu
Hongming Chen
Xin’an Wang
Xing Zhang
Source :
Applied Sciences, Vol 14, Iss 1, p 342 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.24e30fc05ae4c15a02afa78c0f2c118
Document Type :
article
Full Text :
https://doi.org/10.3390/app14010342