An Edge Computing Method for Extracting Pathological Information from Phonocardiogram.
This paper presents a structure of decision support system for pediatric cardiac disease, based on an Internet of Things (IoT) framework. The structure performs the intelligent decision making at its edge processing level, which classifies the heart sound signal, to three classes of cardiac conditio...
| Publicado en: | Studies in Health Technology & Informatics Vol. 262; pp. 364 - 368 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137369865&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137369865 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2019 vid: 262 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 137369865 137369865 137369865 10.3233/SHTI190094 137369865 ppf: 364 ppct: 4 formats: tig: atl: An Edge Computing Method for Extracting Pathological Information from Phonocardiogram. aug: au: GHAREHBAGHI, Arash SEPEHRI, Amir A. BABIC, Ankica affil: School of Innovation, Design and Technology, Mälardalen University, Västerås, Sweden sug: subj: Internet of Things Neural Networks (Computer) Heart Diseases Diagnosis Heart Diseases Pathology Decision Support Systems, Clinical Heart Auscultation Methods Signal Processing, Computer Assisted Pediatric Cardiology Human Iran Echocardiography Infant, Newborn Infant Child, Preschool Child Adolescence Decision Making, Clinical Health Screening Methods Descriptive Statistics Systems Development Medical Records Referral and Consultation Sensitivity and Specificity Health Informatics Infant, Newborn: birth-1 month Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years ab: This paper presents a structure of decision support system for pediatric cardiac disease, based on an Internet of Things (IoT) framework. The structure performs the intelligent decision making at its edge processing level, which classifies the heart sound signal, to three classes of cardiac conditions, normal, mild disease, and critical disease. Three types of the errors are introduced to evaluate the performance of the processing method, Type 1, 2 and 3, defined as the incorrect classification from the critical disease, mild, and normal, respectively. The method is validated using 140 real data patient records collected from the hospital referrals. The estimated negative errors for the Type 1, and 2, are calculated to be 0% and 4.8%, against the positive errors which are 6.3% and 13.3%, respectively. The Type 3, is calculated to be 16.7%, showing a high sensitivity of the method to be used in an IoT healthcare framework. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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