Exploring the Impact of Signal Quality Enhancement on Heart Sound Classification Models

Limited cardiology resources increase the urgency for automated heart disease screening for the general public. Heart sound diagnostic models have been recently employed as a cost-effective solution for the initial screening of heart disease. Noise in heart sound recordings, however, can reduce the performance of such data-driven models. Various quality enhancement approaches have been adopted to alleviate the destructive impact of noise on model performance. One approach isuniversal noise reductionwhich applies denoising techniques to recordings, irrespective of their noise level. The second approach istargeted noise reduction, which applies denoising solely to recordings deemed to need it, based on an assessment of signal noise level. The third approach isfilteringwhere instead of noise reduction, the quality of recordings is assessed and the signals falling below a minimum threshold of quality are discarded. This study aims to understand which quality enhancement approach leads to a more accurate heart sound classification. We developed multiple data-driven models using different classifiers and feature representations and analyzed the impact of quality enhancement on the accuracy of those models. The results indicate that noise reduction is associated with an overall performance drop in classification models. We observe that both universal and targeted noise reduction have a destructive impact on models’ performance. However, filtering improves the accuracy of the models, in particular, for the clinically important abnormal class. The findings of this study can be leveraged to inform the design decisions for the pre-processing of heart sound recordings and consequently optimize downstream classification performance.