Effects of two new features of approximate entropy and sample entropy on cardiac arrest prediction

Sixteen conventional heart beat variability (HRV) parameters and eight vital signs have shown promise in the prediction of cardiac arrest within 72 hours. Besides these 24 parameters, we proposed adding two new features for cardiac arrest prediction, which are approximate entropy (ApEn) and sample entropy (SpEn). ApEn and SpEn are nonlinear HRV parameters capable of characterizing heart conditions. These two entropies were derived from electrocardiography recordings and combined with the existing 24 features to form feature combinations. The experiments were conducted by using linear kernel Support Vector Machine classification technique to investigate the effects of using ApEn, SpEn together with 24 parameters on cardiac arrest prediction. The dimensionality reduction approach, Principal Component Analysis, was applied to suppress the dimensionality. Results reveal that the prediction performance of adding ApEn and SpEn to the 24 parameters is improved significantly compared to using the 24 parameters only. Dimension reduction has additional positive effects on improving the prediction results.