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Article

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

1
School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
2
Tianjin Chest Hospital, Tianjin 300222, China
*
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Sensors 2016, 16(10), 1744; https://doi.org/10.3390/s16101744
Received: 30 August 2016 / Revised: 29 September 2016 / Accepted: 14 October 2016 / Published: 20 October 2016
(This article belongs to the Special Issue Wearable Biomedical Sensors)
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. View Full-Text
Keywords: ECG recognition system; multi-domain features; kernel-independent component analysis; support vector machine ECG recognition system; multi-domain features; kernel-independent component analysis; support vector machine
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MDPI and ACS Style

Li, H.; Yuan, D.; Wang, Y.; Cui, D.; Cao, L. Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System. Sensors 2016, 16, 1744. https://doi.org/10.3390/s16101744

AMA Style

Li H, Yuan D, Wang Y, Cui D, Cao L. Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System. Sensors. 2016; 16(10):1744. https://doi.org/10.3390/s16101744

Chicago/Turabian Style

Li, Hongqiang, Danyang Yuan, Youxi Wang, Dianyin Cui, and Lu Cao. 2016. "Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System" Sensors 16, no. 10: 1744. https://doi.org/10.3390/s16101744

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