  Traditionally, abnormal heart sound classification is framed as a three-stage
process. The first stage involves segmenting the phonocardiogram to detect
fundamental heart sounds; after which features are extracted and classification
is performed. Some researchers in the field argue the segmentation step is an
unwanted computational burden, whereas others embrace it as a prior step to
feature extraction. When comparing accuracies achieved by studies that have
segmented heart sounds before analysis with those who have overlooked that
step, the question of whether to segment heart sounds before feature extraction
is still open. In this study, we explicitly examine the importance of heart
sound segmentation as a prior step for heart sound classification, and then
seek to apply the obtained insights to propose a robust classifier for abnormal
heart sound detection. Furthermore, recognizing the pressing need for
explainable Artificial Intelligence (AI) models in the medical domain, we also
unveil hidden representations learned by the classifier using model
interpretation techniques. Experimental results demonstrate that the
segmentation plays an essential role in abnormal heart sound classification.
Our new classifier is also shown to be robust, stable and most importantly,
explainable, with an accuracy of almost 100% on the widely used PhysioNet
dataset.
