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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.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.relation.projectID | info:eu-repo/grantAgreement/NIH//R01GM104987/ | 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 | S\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 |
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