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

TL;DR

This paper introduces an unsupervised, two-stage method combining dissimilarity matrices and spectral divergence for accurate heartbeat detection and classification in noisy phonocardiogram signals, improving temporal localization and robustness.

Contribution

It presents a novel unsupervised approach that integrates dissimilarity matrices with spectral divergence and a verification-correction process for heartbeat detection and classification.

Findings

Achieves superior detection/classification in noisy clinical scenarios

Effectively preserves cardiac cycle temporal structure

Reduces spurious heart event detection

Abstract

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. 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.