Evaluating and comparing performance of feature combinations of heart rate variability measures for cardiac rhythm classification
Automatic classification of cardiac arrhythmias using heart rate variability (HRV) analysis has been an important research topic in recent years. Explorations reveal that various HRV feature combinations can provide highly accurate models for some rhythm disorders. However, the proposed feature combinations lack a direct and carefully designed comparison. The goal of this work is to assess the various HRV feature combinations in classification of cardiac arrhythmias. In this setting, a total of 56 known HRV features are grouped in eight feature combinations. We evaluate and compare the combinations on a difficult problem of automatic classification between nine types of cardiac rhythms using three classification algorithms: support vector machines, AdaBoosted C4.5, and random forest. The effect of analyzed segment length on classification accuracy is also examined. The results demonstrate that there are three combinations that stand out the most, with total classification accuracy of roughly 85% on time segments of 20s duration. A simple combination of time domain features is shown to be comparable to the more informed combinations, with only 14% worse results on average than the three best ones. Random forest and AdaBoosted C4.5 are shown to be comparably accurate, while support vector machines was less accurate (45%) on this problem. We conclude that the nonlinear features exhibit only a minor influence on the overall accuracy in discerning different arrhythmias. The analysis also shows that reasonably accurate arrhythmia classification lies in the range of 1040s, with a peak at 20s, and a significant drop after 40s.
heart rate variability support vector machines random forest nonlinear features linear features boosted c4.5