基于GAN-CNN的心律失常识别

doi:10.16180/j.cnki.issn1007-7820.2022.03.007

Abstract

Abstract:

ECG analysis is an important basis for doctors to diagnose arrhythmia. The judgment of arrhythmia helps patients understand their physical conditions in time and find potential diseases. However, ECG analysis is not only time-consuming and labor-intensive, but also relies on clinical experience. Therefore, the efficiency of ECG analysis has always been limited by the number of doctors and work efficiency. The development of deep learning technology provides a foundation for the development of computer-aided diagnosis systems. In this study, a one-dimensional ECG signal is converted into a two-dimensional gray image, and a GAN-CNN network is used to solve the problem of ECG data imbalance, which can simultaneously realize the recognition of 7 types of arrhythmia and normal heartbeat. The experiment is verified by the MIT-BIH arrhythmia database. The average accuracy rate reaches 99.32%, and the sensitivity and specificity are 99.69% and 98.91%, respectively.

Key words: electrocardiogram, arrhythmia, deep learning, auxiliary diagnosis, electrocardiograph signal, GAN-CNN, two-dimensional image, unbalanced electrocardiogram data

CLC Number:

TP183

Peng CHEN,Zilong LIU. Arrhythmia Recognition Based on GAN-CNN[J].Electronic Science and Technology, 2022, 35(3): 45-50.

Figures/Tables 6

Table 1

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Table 2

References 20

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类型	记录	数量
FVN	108,109,114,124,200,201,202,203,205,208,210 213,214,215,219,223,233	803
LBBB	109,111,207,213	8 072
NOR	100,101,103,105,108,112,113,114,115,117,121 122,123,202,205,219,230,234	75 052
PAB	102,104,107,217	7 024
PVC	106,116,119,200,201,203,208,210,213,215,221 228,233	7 130
RBBB	118,124,212,231	7 256
VEB	207	106
VFW	207	472

方法	类别	数据集大小	精确度 /%	敏感性 /%	特异性 /%	阳性 /%
GAN-CNN (DA+Swish)	8	44 000	99.32	99.69	98.91	99.84
GAN-CNN (Swish)	8	28 881	97.95	98.82	98.55	99.65
GAN-CNN (DA+ReLU)	8	44 000	99.17	99.61	98.55	99.79
VGG16(DA)	8	44 000	98.69	99.29	98.27	99.75
MFCNN^[15]	5	109 449	95.48	96.53	87.74	-
SVM^[16]	2	1 247	98.48	98.90	98.04	98.13
SVM+KNN^[17]	7	7 185	98.06	-	-	-
LSTM+CNN^[18]	5	94 131	99.11	97.27	99.44	-
CNN^[19]	5	452 960	94.03	96.71	91.54	97.86
2-DCNN^[20]	5	27 789	97.42	-	-	-

Arrhythmia Recognition Based on GAN-CNN

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