Abstract
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.
Highlights
With the progress of science and technology, automatic analysis and diagnosis systems based on electrocardiogram (ECG) signals have been extensively investigated to detect and diagnose cardiac diseases [1,2,3,4,5]
Multi-Domain Feature Extraction Using kernel-independent component analysis (KICA) Combined with discrete wavelet transform (DWT)
principle component analysis (PCA) is employed to reduce the dimensions of ECG sample data in the nonlinear feature extraction, and linear discriminant analysis (LDA) is applied to reduce the dimensions of the frequency domain features
Summary
With the progress of science and technology, automatic analysis and diagnosis systems based on electrocardiogram (ECG) signals have been extensively investigated to detect and diagnose cardiac diseases [1,2,3,4,5]. Pre-processing of ECG signals is necessary to reduce various interferences. Other mathematical morphological algorithms are applied in signal pre-processing [11,12]. Wavelet transform (WT) method based on its good time-frequency property for signal denoising have been popularly developed and applied to eliminate noise effectively in ECG identification system [13,14,15,16]. An approach based on empirical mode decomposition and an improved approximate envelope method was presented for ECG signal processing [18]
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