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

• Published in: Computer methods and programs in biomedicine 2022-06, Vol.221, p.106909-106909, Article 106909
• Main Authors: Torre-Cruz, J., Martinez-Muñoz, D., Ruiz-Reyes, N., Muñoz-Montoro, A.J., Puentes-Chiachio, M., Canadas-Quesada, F.J.
• Format: Article
• Language: English
• Subjects: Detection/classification; Dissimilarity; Divergence; Heartbeat S1/S2; Phonocardiography; Unsupervised
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106909

•An unsupervised approach is proposed to the detection/classification of the heartbeats from phonocardiogram (PCG) signals.•The dissimilarity matrix with the frame-level spectral divergence is a promising tool to estimate heartbeats in PCG signals.•The experimental results have been conducted using several open access databases and a commercial clinical noise database.•The proposed method achieves a significant improvement in heart detection/classification compared to other evaluated methods.•The proposed method shows higher robustness evaluating different types of clinical environmental noises. Background 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 non-invasiveness. 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-correction-classification 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 th