基于双谱和谱特征的心电信号分类方法

doi:10.16180/j.cnki.issn1007-7820.2021.05.008

Abstract

Abstract:

As the lowest high-order spectrum, bispectrum has many excellent characteristics, but it also has the defect of large calculation. Based on bispectrum matrix, a feature extraction method for two-dimensional ECG signal is proposed in the present study. The spectral flatness, spectral brightness and spectral roll off are combined to form the eigenvector, which is further combined with the support vector machine classification method founded on radial basis kernel function to realize the classification and recognition of ECG signals. The ECG signal in MIT-BIH database is applied to verify the method. Experimental result show that the two-dimensional spectrum feature extraction method based on bispectrum proposed in this study has a small amount of computation and an accuracy of 93.4%, indicating that the proposed method can effectively realize the diagnosis of arrhythmia and the classification of ECG signals.

Key words: ECG signals, feature extraction, bispectrum analysis, spectrum flatness, spectral brightness, spectral roll-off, classification recognition, support vector machine

CLC Number:

TP274

LIU Shu,SHAO Jie,ZHANG Yiting,ZHANG Shanzhang. ECG Classification Based on Bispectrum and Spectral Features[J].Electronic Science and Technology, 2021, 34(5): 42-46.

Figures/Tables 5

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Table 1

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References 18

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实际类型	分类结果
实际类型	N	S	V	F	合计
N	30 288	1 247	1 599	863	33 997
S	187	965	99	37	1 288
V	297	119	1 643	66	2 125
F	54	18	9	311	392

实际类型	分类结果
实际类型	S	V	F	合计
S	1 207	68	13	1 288
V	111	1 963	51	2 125
F	1	7	384	392

序号	特征	总识别率/%
文献[14]	QRS复合波	89.0
文献[15]	RR间隔	92.7
文献[16]	小波	92.7
文献[17]	线性预测系数	93.1
本文	双谱	93.4
文献[18]	QRS复合波	95.1

ECG Classification Based on Bispectrum and Spectral Features

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