A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection without Segmentation

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...

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Bibliographic Details
Main Authors:	Denman, S. (Author), Dissanayake, T. (Author), Fernando, T. (Author), Fookes, C. (Author), Ghaemmaghami, H. (Author), Sridharan, S. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	algorithm Algorithms Anomaly detection Article artificial intelligence Artificial Intelligence artificial neural network Biomedical signal processing calibration Cardiology Classification (of information) classifier Computational burden congenital heart malformation deep learning Deep learning Deep Learning electroencephalography Extraction feature extraction Feature extraction Heart heart sound heart sound segmentation Heart sounds human learning algorithm Learning classifiers machine learning Medical domains Model interpretations nerve cell network neural networks phonocardiogram Phonocardiograms phonocardiography Phonocardiography PhysioNet qualitative analysis signal processing Signal Processing, Computer-Assisted sound detection speech intelligibility steady state support vector machine Three-stage process training waveform
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A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection without Segmentation

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