18:15 - 20:15 | Mon 5 Mar | Caribbean ABC | MoPO.34
In this paper, a novel method is proposed to predict upcoming heart abnormalities by processing electrocardiogram (ECG) signals. The core idea behind the proposed method is to use a controlled nonlinear transformation to project extracted signal features into a higher-order dimensional space with desired geometric properties. In particular, we enforce the projected clusters of different abnormalities to symmetrically encircle the normal cluster through penalizing the clustering non-symmetry. An immediate utility of this method is to characterize the deviation of ECG signal samples from the patient-specific norms towards different abnormality classes. Moreover, this method can be used to enhance our prediction about the potential upcoming heart problems before their occurrences. This is a critical point, since a timely diagnosis and therapeutic intervention can significantly reduce the heart-related mortality rate. We applied this method to publicly available MIT-BIH dataset with 3 abnormality classes and the results suggest, respectively, 8%, 9% and 12% improvement in predicting the three abnormality classes. The proposed framework is general and applicable to a broad range of biomedical signals.