Does Effective Heart Sound Segmentation Necessarily Require Deep Learning?

Are complex Deep Learning (DL) models necessary for effective heart sound segmentation? This paper demonstrates that an effective denoising approach combined with a standard lowcost probabilistic Markov-based segmentation method provides performance that is comparable to DL models while maintaining reasonable numerical complexity. The proposed pipeline takes advantage of the pseudo-periodicity of heart events across cardiac cycles for an efficient denoising step. This preprocessing step is reformulated as a constrained low-rank matrix inference in the framework of a graph signal processing. Denoised heart sounds are then fed to the classical Logistic Regression Hidden Semi-Markov Model (LR-HSMM) for segmentation purposes. A comparative study of the proposed pipeline with one of the most effective DL models, namely CNN-LSTM, using the CirCor dataset containing both healthy and murmur PCG signals was conducted to emphasize our purpose. Obtained results in terms of both segmentation of the two primary heart sounds S1 and S2 and murmur classification of this two-step pipeline are quasiequivalent to those obtained using the CNN-LSTM model, but with a relatively lower numerical complexity.