Heart disease classification through HRV analysis using Parallel Cascade Identification and Fast Orthogonal Search

Heart rate variability (HRV) is an established indicator of cardiac health. Recent developments have shown the potential of nonlinear metrics for pattern classification of various heart conditions. Evidence indicates that the combination of multiple linear and nonlinear features leads to increased classification accuracy. In our paper, we demonstrate HRV classification using two dynamic nonlinear techniques called Parallel Cascade Identification (PCI) and Fast Orthogonal Search (FOS). We investigate the use of these two techniques for feature extraction from publicly available Physionet electrocardiogram (ECG) data to differentiate between normal sinus rhythm of the heart and 3 undesired conditions: arrhythmia, supraventricular arrhythmia, and congestive heart failure. Results compare well with previous studies which have used more features over the same dataset. We hypothesize that combining PCI and FOS features with traditional HRV features will show further improvement in classification accuracy and so can assist in real-time patient monitoring.