The Feasibility of Predicting Impending Malignant Ventricular Arrhythmias on the basis of Signal Complexity of Heartbeat Intervals

For calculating entropy values and predicting MAs as early as possible (which is the aim of this study), two specifications are of interest: the minimal length of HbI needed to delineate the MAs patterns sufficiently (), and the maximum time length at which our model can predict impending MAs (). We compared the RF model with support vector machine (SVM) models based on linear and Gaussian kernels. Results show that the RF model performs the best, reaching a 99.24% recall and a 99.87% precision for a HbI of 500 heartbeats (the ) 374 seconds (the ) preceding the occurrence of MAs. The HbI samples in this study were extracted from an electrocardiograph (ECG). However, given the subtle difference (0.1 ms typically) between the R-R interval of ECG and the P-P interval of PPG, this approach could be extended to HbI acquired by the PPG sensor and thus should be of substantial theoretical and practical significance in cardiac arrest prevention.