Automated signal quality assessment of mobile phone-recorded heart sound signals.
Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiat...
| Publicado en: | Journal of Medical Engineering & Technology Vol. 40; no. 7/8; pp. 342 - 356 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
Taylor & Francis Ltd
Oct/Nov2016
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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=119745629&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119745629 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03091902 B9Q jtl: Journal of Medical Engineering & Technology issn: 03091902 maglogo: Y pubinfo: dt: Oct/Nov2016 vid: 40 iid: 7/8 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 119745629 119745629 NLMB9Q20161001-00002 119745629 10.1080/03091902.2016.1213902 NLM27659352 119745629 ppf: 342 ppct: 14 formats: tig: atl: Automated signal quality assessment of mobile phone-recorded heart sound signals. aug: au: Springer, David B. Brennan, Thomas Ntusi, Ntobeko Abdelrahman, Hassan Y. Zühlke, Liesl J. Mayosi, Bongani M. Tarassenko, Lionel Clifford, Gari D. affil: Department of Engineering Science, University of Oxford, Oxford, UK sug: subj: Signal Processing, Computer Assisted Heart Sounds Algorithms Middle Age Reproducibility of Results Aged Female Adult Telemedicine Heart Auscultation Male Human Middle Aged: 45-64 years Aged: 65+ years Adult: 19-44 years Female Male ab: Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiate poor quality from diagnostically useful recordings since non-experts are more likely to make poor-quality recordings. This paper investigates the automated signal quality assessment of heart sound recordings performed using both mobile phone-based and commercial medical-grade electronic stethoscopes. The recordings, each 60 s long, were taken from 151 random adult individuals with varying diagnoses referred to a cardiac clinic and were professionally annotated by five experts. A mean voting procedure was used to compute a final quality label for each recording. Nine signal quality indices were defined and calculated for each recording. A logistic regression model for classifying binary quality was then trained and tested. The inter-rater agreement level for the stethoscope and mobile phone recordings was measured using Conger’s kappa for multiclass sets and found to be 0.24 and 0.54, respectively. One-third of all the mobile phone-recorded phonocardiogram (PCG) signals were found to be of sufficient quality for analysis. The classifier was able to distinguish good- and poor-quality mobile phone recordings with 82.2% accuracy, and those made with the electronic stethoscope with an accuracy of 86.5%. We conclude that our classification approach provides a mechanism for substantially improving auscultation recordings by non-experts. This work is the first systematic evaluation of a PCG signal quality classification algorithm (using a separate test dataset) and assessment of the quality of PCG recordings captured by non-experts, using both a medical-grade digital stethoscope and a mobile phone. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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