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Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals

dc.contributor.authorDe La Torre Cruz, Juan
dc.contributor.authorMartínez Muñoz, Damián
dc.contributor.authorRuiz Reyes, Nicolás
dc.contributor.authorMuñoz Montoro, Antonio Jesús
dc.contributor.authorPuentes Chiachio, Miguel
dc.contributor.authorCanadas Quesada, Francisco Jesús
dc.date.accessioned2024-10-30T09:44:37Z
dc.date.available2024-10-30T09:44:37Z
dc.date.issued2022-06
dc.description.abstractBackground and objective: Auscultation is the first technique applied to the early diagnose of any cardiovascular disease (CVD) in rural areas and poor-resources countries because of its low cost and noninvasiveness. However, it highly depends on the physician’s expertise to recognize specific heart sounds heard through the stethoscope. The analysis of phonocardiogram (PCG) signals attempts to segment each cardiac cycle into the four cardiac states (S1, systole, S2 and diastole) in order to develop automatic systems applied to an efficient and reliable detection and classification of heartbeats. In this work, we propose an unsupervised approach, based on time-frequency characteristics shown by cardiac sounds, to detect and classify heartbeats S1 and S2. Methods: The proposed system consists of a two-stage cascade. The first stage performs a rough heartbeat detection while the second stage refines the previous one, improving the temporal localization and also classifying the heartbeats into types S1 and S2. The first contribution is a novel approach that combines the dissimilarity matrix with the frame-level spectral divergence to locate heartbeats using the repetitiveness shown by the heart sounds and the temporal relationships between the intervals defined by the events S1/S2 and non-S1/S2 (systole and diastole). The second contribution is a verification-correctionclassification process based on a sliding window that allows the preservation of the temporal structure of the cardiac cycle in order to be applied in the heart sound classification. The proposed method has been assessed using the open access databases PASCAL, CirCor DigiScope Phonocardiogram and an additional sound mixing procedure considering both Additive White Gaussian Noise (AWGN) and different kinds of clinical ambient noises from a commercial database. Results: The proposed method outperforms the detection and classification performance of other recent state-of-the-art methods. Although our proposal achieves the best average accuracy for PCG signals without cardiac abnormalities, 99.4% in heartbeat detection and 97.2% in heartbeat classification, its worst average accuracy is always above 92% for PCG signals with cardiac abnormalities, signifying an improvement in heartbeat detection/classification above 10% compared to the other state-of-the-art methods evaluated. Conclusions: The proposed method provides the best detection/classification performance in realistic scenarios where the presence of cardiac anomalies as well as different types of clinical environmental noises are active in the PCG signal. Of note, the promising modelling of the temporal structures of the heart provided by the dissimilarity matrix together with the frame-level spectral divergence, as well as the removal of a significant number of spurious heart events and recovery of missing heart events, both corrected by the proposed verification-correction-classification algorithm, suggest that our proposal is a successful tool to be applied in heart segmentation.es_ES
dc.description.sponsorshipThis work was supported by the Programa Operativo FEDER Andalucia 2014–2020 under project with reference 1257914, the Ministry of Economy, Knowledge and University, Junta de Andalucia under Project P18-RT-1994 and the Spanish Ministry of Science and Innovation under Project PID2020-119082RB-C21.es_ES
dc.identifier.citationTorre-Cruz J., Martinez-Muñoz D., Ruiz-Reyes N., Muñoz-Montoro A.J., Puentes-Chiachio M., Canadas-Quesada F.J. Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals. Computer Methods and Programs in Biomedicine. vol. 221, June 2022, 106909. https://doi.org/10.1016/j.cmpb.2022.106909es_ES
dc.identifier.issn0169-2607es_ES
dc.identifier.otherhttps://doi.org/10.1016/j.cmpb.2022.106909es_ES
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0169260722002917?via%3Dihubes_ES
dc.identifier.urihttps://hdl.handle.net/10953/3337
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.ispartofComputer Methods and Programs in Biomedicinees_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectPhonocardiographyes_ES
dc.subjectUnsupervisedes_ES
dc.subjectDetection/classificationes_ES
dc.subjectHeartbeat S1/S2es_ES
dc.subjectDissimilarityes_ES
dc.subjectDivergencees_ES
dc.titleUnsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signalses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES

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