Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

Sensors. 2016;16(10):1744 DOI 10.3390/s16101744

 

Journal Homepage

Journal Title: Sensors

ISSN: 1424-8220 (Print)

Publisher: MDPI AG

LCC Subject Category: Technology: Chemical technology

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS

Hongqiang Li (School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China)
Danyang Yuan (School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China)
Youxi Wang (School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China)
Dianyin Cui (School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China)
Lu Cao (Tianjin Chest Hospital, Tianjin 300222, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.