K. P. Cohen, W. Tompkins, A. Djohan, J. Webster, Y.H. Hu
{"title":"QRS检测采用模糊神经网络","authors":"K. P. Cohen, W. Tompkins, A. Djohan, J. Webster, Y.H. Hu","doi":"10.1109/IEMBS.1995.575064","DOIUrl":null,"url":null,"abstract":"We developed a QRS detection algorithm which uses a fuzzy neural network (FNN) to process lead II recordings of the ECG. We trained and tested our algorithm using the MIT/BIH arrhythmia database, and compared our results to existing algorithms. For tapes 100, 105 and 108, our FNN reduced the total number of combined false-positive and false-negative detections from 174 to 44.","PeriodicalId":20509,"journal":{"name":"Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1995-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"QRS detection using a fuzzy neural network\",\"authors\":\"K. P. Cohen, W. Tompkins, A. Djohan, J. Webster, Y.H. Hu\",\"doi\":\"10.1109/IEMBS.1995.575064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We developed a QRS detection algorithm which uses a fuzzy neural network (FNN) to process lead II recordings of the ECG. We trained and tested our algorithm using the MIT/BIH arrhythmia database, and compared our results to existing algorithms. For tapes 100, 105 and 108, our FNN reduced the total number of combined false-positive and false-negative detections from 174 to 44.\",\"PeriodicalId\":20509,\"journal\":{\"name\":\"Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMBS.1995.575064\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1995.575064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We developed a QRS detection algorithm which uses a fuzzy neural network (FNN) to process lead II recordings of the ECG. We trained and tested our algorithm using the MIT/BIH arrhythmia database, and compared our results to existing algorithms. For tapes 100, 105 and 108, our FNN reduced the total number of combined false-positive and false-negative detections from 174 to 44.
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