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A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation
[article]
2020
arXiv
pre-print
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
arXiv:2005.10480v2
fatcat:chd2ywgsivdbddcarmvrd4tszu