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