{"title":"基于PCG信号分析的智能算法在心脏病诊断中的应用","authors":"Mohammed Nabih-Ali, E. El-Dahshan, Ashraf S Yahia","doi":"10.4236/CS.2017.87012","DOIUrl":null,"url":null,"abstract":"This paper presents an intelligent algorithm for heart diseases diagnosis using \nphonocardiogram (PCG). The proposed technique consists of four stages: \nData acquisition, pre-processing, feature extraction and classification. PASCAL \nheart sound database is used in this research. The second stage concerns with \nremoving noise and artifacts from the PCG signals. Feature extraction stage is \ncarried out using discrete wavelet transform (DWT). Finally, artificial neural \nnetwork (ANN) has been used for classification stage with an overall accuracy \n97%.","PeriodicalId":63422,"journal":{"name":"电路与系统(英文)","volume":"08 1","pages":"184-190"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":"{\"title\":\"Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis\",\"authors\":\"Mohammed Nabih-Ali, E. El-Dahshan, Ashraf S Yahia\",\"doi\":\"10.4236/CS.2017.87012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an intelligent algorithm for heart diseases diagnosis using \\nphonocardiogram (PCG). The proposed technique consists of four stages: \\nData acquisition, pre-processing, feature extraction and classification. PASCAL \\nheart sound database is used in this research. The second stage concerns with \\nremoving noise and artifacts from the PCG signals. Feature extraction stage is \\ncarried out using discrete wavelet transform (DWT). Finally, artificial neural \\nnetwork (ANN) has been used for classification stage with an overall accuracy \\n97%.\",\"PeriodicalId\":63422,\"journal\":{\"name\":\"电路与系统(英文)\",\"volume\":\"08 1\",\"pages\":\"184-190\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"电路与系统(英文)\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.4236/CS.2017.87012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"电路与系统(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/CS.2017.87012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis
This paper presents an intelligent algorithm for heart diseases diagnosis using
phonocardiogram (PCG). The proposed technique consists of four stages:
Data acquisition, pre-processing, feature extraction and classification. PASCAL
heart sound database is used in this research. The second stage concerns with
removing noise and artifacts from the PCG signals. Feature extraction stage is
carried out using discrete wavelet transform (DWT). Finally, artificial neural
network (ANN) has been used for classification stage with an overall accuracy
97%.