{"title":"基于BP算法的MIT-BIH心律失常分类与处理","authors":"Fumin Mi, Baixuan Li, Xiaojie Cheng, Yunjie Zhao, Minyi Li, Jin Jing","doi":"10.1109/ISBP57705.2023.10061303","DOIUrl":null,"url":null,"abstract":"ECG recognition is of great significance to the diagnosis of heart diseases. Based on the data of the MIT-BIH Arrhythmia Database, a more accurate ECG signal map was extracted using wavelet transform, a BP neural network was constructed for pattern recognition, and five types of arrhythmia-sinus arrhythmia, premature beats, Yibo, sinoatrial block, and atrial block. And compared with the BP network using SVM and K nearest neighbor algorithm, it is found that the BP network performs better.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"11 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification and Processing of MIT-BIH Arrhythmia-Based on BP Algorithm\",\"authors\":\"Fumin Mi, Baixuan Li, Xiaojie Cheng, Yunjie Zhao, Minyi Li, Jin Jing\",\"doi\":\"10.1109/ISBP57705.2023.10061303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ECG recognition is of great significance to the diagnosis of heart diseases. Based on the data of the MIT-BIH Arrhythmia Database, a more accurate ECG signal map was extracted using wavelet transform, a BP neural network was constructed for pattern recognition, and five types of arrhythmia-sinus arrhythmia, premature beats, Yibo, sinoatrial block, and atrial block. And compared with the BP network using SVM and K nearest neighbor algorithm, it is found that the BP network performs better.\",\"PeriodicalId\":309634,\"journal\":{\"name\":\"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)\",\"volume\":\"11 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBP57705.2023.10061303\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBP57705.2023.10061303","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification and Processing of MIT-BIH Arrhythmia-Based on BP Algorithm
ECG recognition is of great significance to the diagnosis of heart diseases. Based on the data of the MIT-BIH Arrhythmia Database, a more accurate ECG signal map was extracted using wavelet transform, a BP neural network was constructed for pattern recognition, and five types of arrhythmia-sinus arrhythmia, premature beats, Yibo, sinoatrial block, and atrial block. And compared with the BP network using SVM and K nearest neighbor algorithm, it is found that the BP network performs better.
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