Impact of Preprocessing on the Performance of Heart Sound Segmentation

Accurate segmentation of heart sound signals (phonocardiograms, PCGs) is a critical step for the early diagnosis of cardiovascular diseases (CVDs). Although deep learning models, particularly convolutional neural networks (CNNs) like the UNet, have achieved strong performance in PCG segmentation, the impact of signal preprocessing remains underexplored. In this study, we evaluate how different preprocessing strategies, namely wavelet-based denoising, Butterworth filtering, and their combination, affect the segmentation performance of a 1 D UNet model. Using the PhysioNet 2016 database, we evaluated segmentation quality based on sample accuracy, positive predictive value, and sensitivity. The results show that minimal preprocessing, specifically Butterworth bandpass filtering alone, yields the best segmentation performance, outperforming more aggressive preprocessing pipelines. These findings highlight that preserving the baseline structure of PCG signals is crucial for optimal learning and that lightweight preprocessing remains an essential consideration, especially when applying modern deep learning architectures.