A. Amiri, G. Armano, Amir-Mohammad Rahmani, K. Mankodiya
{"title":"PhonoSys: Mobile Phonocardiography Diagnostic System for Newborns","authors":"A. Amiri, G. Armano, Amir-Mohammad Rahmani, K. Mankodiya","doi":"10.4108/eai.14-10-2015.2261614","DOIUrl":null,"url":null,"abstract":"Heart murmurs have been found to be a life threatening condition for the newborns who are born with cardiac abnormalities. The first sign of pathological changes of heart valves appears in phonocardiogram which contains very useful information about cardiovascular system. It is a challenging venture to distinguish pathological murmurs from innocent ones. In this paper we have developed a diagnostic algorithm called PhonoSys to analyze PCG using random forest. PhonoSys algorithm will run on mobile devices for remote PCG analysis. We recorded PCG signals from 120 newborns who are either healthy or with cardiac abnormalities. Eventually, in this study, 97.6% accuracy, 96.8% sensitivity, and 98.4% specicity were obtained to classify between innocent and pathological murmurs.","PeriodicalId":299985,"journal":{"name":"EAI Endorsed Trans. Mob. Commun. Appl.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Trans. Mob. Commun. Appl.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eai.14-10-2015.2261614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Heart murmurs have been found to be a life threatening condition for the newborns who are born with cardiac abnormalities. The first sign of pathological changes of heart valves appears in phonocardiogram which contains very useful information about cardiovascular system. It is a challenging venture to distinguish pathological murmurs from innocent ones. In this paper we have developed a diagnostic algorithm called PhonoSys to analyze PCG using random forest. PhonoSys algorithm will run on mobile devices for remote PCG analysis. We recorded PCG signals from 120 newborns who are either healthy or with cardiac abnormalities. Eventually, in this study, 97.6% accuracy, 96.8% sensitivity, and 98.4% specicity were obtained to classify between innocent and pathological murmurs.