- -

Recent advances in heart sound analysis

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Recent advances in heart sound analysis

Show simple item record

Files in this item

dc.contributor.author Clifford, Gari D. es_ES
dc.contributor.author Liu, Chengyu es_ES
dc.contributor.author Moody, Benjamin es_ES
dc.contributor.author Millet Roig, José es_ES
dc.contributor.author Schmidt, Samuel es_ES
dc.contributor.author Li, Qiao es_ES
dc.contributor.author Silva, Ikaro es_ES
dc.contributor.author Mark, Roger G. es_ES
dc.date.accessioned 2020-10-21T03:31:27Z
dc.date.available 2020-10-21T03:31:27Z
dc.date.issued 2017-08-01 es_ES
dc.identifier.issn 0967-3334 es_ES
dc.identifier.uri http://hdl.handle.net/10251/152717
dc.description "This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at https://doi.org/10.1088/1361-6579/aa7ec8". es_ES
dc.description.abstract [EN] Objective: Auscultation of heart sound recordings or the phonocardiogram (PCG) has been shown to be valuable for the detection of disease and pathologies (Leatham 1975, Raghu et al 2015). The automated classification of pathology in heart sounds has been studied for over 50 years. Typical methods can be grouped into: artificial neural network-based approaches (Uguz 2012), support vector machines (Ari et al 2010), hidden Markov model-based approaches (Saracoglu 2012) and clustering-based approaches (Quiceno-Manrique et al 2010). However, accurate automated classification still remains a significant challenge due to the lack of highquality, rigorously validated, and standardized open databases of heart sound recordings. Approach: The 2016 PhysioNet/Computing in Cardiology (CinC) Challenge sought to create a large database to facilitate this, by assembling recordings from multiple research groups across the world, acquired in different real-world clinical and nonclinical environments (such as in-home visits), to encourage the development of algorithms to accurately identify, from a single short recording (10-60s), as normal, abnormal or poor signal quality, and thus to further identify whether the subject of the recording should be referred on for an expert diagnosis (Liu et al 2016). Until this Challenge, no significant open-access heart sound database was available for researchers to train and evaluate the automated diagnostics algorithms upon (Clifford et al 2016). Moreover, no open source heart sound segmentation and classification algorithms were available. The Challenge changed this situation significantly. Main results and Significance: This editorial reviews the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for promising research avenues in the field of heart sound signal processing and classification as a result of the Challenge. es_ES
dc.description.sponsorship This work was funded in part by the National Institutes of Health, grant R01-GM104987, the International Postdoctoral Exchange Programme of the National Postdoctoral Management Committee of China and Emory University. We are also grateful to Mathworks for providing free software licenses and sponsoring the Challenge prize money, and Computing in Cardiology for sponsoring the Challenge prize money and providing a forum to present the Challenge results. We would also like to thank the database contributors, and data annotators for their invaluable assistance. Finally, we would like to thank all the competitors and researchers themselves, without whom there would be no Challenge or special issue. es_ES
dc.language Inglés es_ES
dc.publisher IOP Publishing es_ES
dc.relation NIH/R01-GM104987 es_ES
dc.relation.ispartof Physiological Measurement es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Heart sound es_ES
dc.subject Signal processing es_ES
dc.subject Physiological es_ES
dc.subject Measurement es_ES
dc.subject Adquisition es_ES
dc.subject Detection es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Recent advances in heart sound analysis es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1088/1361-6579/aa7ec8 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Clifford, GD.; Liu, C.; Moody, B.; Millet Roig, J.; Schmidt, S.; Li, Q.; Silva, I.... (2017). Recent advances in heart sound analysis. Physiological Measurement. 38(8):10-25. https://doi.org/10.1088/1361-6579/aa7ec8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1088/1361-6579/aa7ec8 es_ES
dc.description.upvformatpinicio 10 es_ES
dc.description.upvformatpfin 25 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 38 es_ES
dc.description.issue 8 es_ES
dc.identifier.pmid 28696334 es_ES
dc.relation.pasarela 360450 es_ES
dc.contributor.funder Mathworks es_ES
dc.contributor.funder Emory University es_ES
dc.contributor.funder National Institutes of Health, EEUU es_ES
dc.contributor.funder National Postdoctoral Management Committee of China es_ES


This item appears in the following Collection(s)

Show simple item record