Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments

• URL: http://arxiv.org/abs/2006.02610v1
• Date: Thu, 4 Jun 2020 02:07:35 GMT
• Title: Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments
• Authors: Balagopal Unnikrishnan, Pranshu Ranjan Singh, Xulei Yang, and Matthew Chin Heng Chua
• Abstract summary: This study uses various supervised, semi-supervised and unsupervised approaches on the PhysioNet/CinC 2016 Challenge dataset. A GAN based semi-supervised method is proposed, which allows the usage of unlabelled data samples to boost the learning of data distribution. In particular, the unsupervised feature extraction using 1D CNN Autoencoder coupled with one-class SVM obtains good performance without any data labelling.
• Score: 4.712158833534046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated heart sounds classification is a much-required diagnostic tool in the view of increasing incidences of heart related diseases worldwide. In this study, we conduct a comprehensive study of heart sounds classification by using various supervised, semi-supervised and unsupervised approaches on the PhysioNet/CinC 2016 Challenge dataset. Supervised approaches, including deep learning and machine learning methods, require large amounts of labelled data to train the models, which are challenging to obtain in most practical scenarios. In view of the need to reduce the labelling burden for clinical practices, where human labelling is both expensive and time-consuming, semi-supervised or even unsupervised approaches in restricted data setting are desirable. A GAN based semi-supervised method is therefore proposed, which allows the usage of unlabelled data samples to boost the learning of data distribution. It achieves a better performance in terms of AUROC over the supervised baseline when limited data samples exist. Furthermore, several unsupervised methods are explored as an alternative approach by considering the given problem as an anomaly detection scenario. In particular, the unsupervised feature extraction using 1D CNN Autoencoder coupled with one-class SVM obtains good performance without any data labelling. The potential of the proposed semi-supervised and unsupervised methods may lead to a workflow tool in the future for the creation of higher quality datasets.

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