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Computational analysis on physiological signals would provide immense impactfor enabling automated clinical analytics. However, the class imbalance issuewhere negative or minority class instances are rare in number impairs therobustness of the practical solution. The key idea of our approach isintelligent augmentation of minority class examples to construct smooth,unbiased decision boundary for robust semi-supervised learning. This solves thepractical class imbalance problem in anomaly detection task for computationalclinical analytics using physiological signals. We choose two critical cardiacmarker physiological signals: Heart sound or Phonocardiogram (PCG) andElectrocardiogram (ECG) to validate our claim of robust anomaly detection ofclinical events under augmented class learning, where intelligent synthesis ofminority class instances attempt to balance the class distribution. We performextensive experiments on publicly available expert-labelled MIT-Physionet PCGand ECG datasets that establish high performance merit of the proposed scheme,and our scheme fittingly performs better than the state-of-the-art algorithms.

Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals