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

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.

Keywords: ECG recognition system; kernel-independent component analysis; multi-domain features; support vector machine.

Figure 1

Block scheme of the proposed electrocardiogram (ECG) recognition system for ECG beats classification. The presented system is composed of ECG pre-processing, feature extraction and classification. ECG pre-processing removes noise and interference from original ECG beats. Feature extraction derives multi-domain features through kernel-independent component analysis (KICA) and discrete wavelet transform (DWT). The support vector machine (SVM) classifier, optimized with genetic algorithm (GA), divides ECG beats into five categories: normal beat (N), left bundle branch block beat (LBBB), right bundle branch block beat (RBBB), premature ventricular contraction (PVC) and atrial premature beat (APC).

Figure 2

Results of wavelet transform (WT) for ECG denoising. (a1–a5) present the approximation coefficients of WT, and (d1–d5) present the detail coefficients of WT.

Figure 3

Denoising results of different threshold functions. (a) Original signal; (b) Noisy signal; (c) Signal denoised by the soft threshold function; (d) Signal denoised by the hard threshold function; and (e) Signal denoised by the improved threshold function.

Figure 4

Spectrum of different signals. (a) Spectrum of the original signal; (b) Spectrum of the noisy signal; (c) Spectrum of the signal denoised by the soft threshold function; (d) Spectrum of the signal denoised by the hard threshold function; (e) Spectrum of the signal denoised by the improved threshold function.

Figure 5

Feature subspace obtained through kernel-independent component analysis (KICA). (s1–s20) are 20 independent base signals, x-axis represents sample points of the ECG signal and y-axis is the amplitude.

Figure 6

Frequency domain features of five types of ECG beats obtained through DWT. s0 is the original ECG beat, ca4 is the approximation of the 4th level and cd1 to cd4 are the details of each level. (a) ECG of record 100 is used to represent N; (b) ECG of record 109 represents LBBB; (c) ECG of record 118 represents RBBB; (d) ECG of record 106 represents PVC; and (e) ECG of record 209 represents APC.

Figure 7

Diagrams of the ECG acquisition experimental platform. (a) Schematic of the experimental platform; and (b) Construction of the experimental platform.

Figure 8

Fitness curve of GA for finding the optimal parameters of SVM. Average fitness and best fitness are gradually increased via a series of iterations. When the evolution algebra is 200, average fitness and best fitness reach the maximum value, namely, the final optimization parameters of SVM are obtained.

Figure 9

Five types of ECG signals acquired by the experimental platform. The ECG signals in the red dashed boxes are the five types of the acquired beats. (a) N; (b) LBBB; (c) RBBB; (d) PVC; and (e) APC.