A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification

BMC Medical Informatics and Decision Making

Lei Wang¹  Kai Wu²  Weikang Xu³  Zhongwei Tan³  Fei Xu³  Guixiang Li⁴  Jun Chen⁴ 

[1] Department of Artificial Intelligence, College of Information and Communication Engineering, Hainan University, 570228, Haikou, China;Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, 510006, Guangzhou, China;Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, 510500, Guangzhou, China;Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, 510370, Guangzhou, China;The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, 510370, Guangzhou, China;National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, 510006, Guangzhou, China;Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, 510006, Guangzhou, China;Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, 980-8575, Sendai, Japan;National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, 510500, Guangzhou, China;National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, 510500, Guangzhou, China;Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, 510500, Guangzhou, China;

关键词: Abnormal ECG identification;    BP neural network;    Wavelet analysis;    Principal component analysis;    Particle swarm optimization;   

DOI  :  10.1186/s12911-021-01453-6

来源: Springer

【 摘 要 】

BackgroundAs proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What’s more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease.MethodsIn this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension.ResultsWavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model.ConclusionIn summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.

【 授权许可】

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