Bradycardia and Tachycardia Detection Using a Synthesis-by-Analysis Modeling Approach of Pulsatile Signal

Bradycardia and tachycardia reflect abnormalities of the heart that can lead to severe harm to the cardiovascular system. The pulsatile signal is a useful tool to detect these two kinds of arrhythmias. In this study, we present a pulsatile synthesis-by-analysis (PSA) modeling based method to detect bradycardia and tachycardia. A new PSA modeling method was proposed to quantitively describe the changes of pulsatile waves, and we obtained twelve parameters for constructing a feature vector from the PSA model of each wave, by which we trained classifiers of probabilistic neural network (PNN) and random forest (RF). Our experiments were performed on the Fantasia and 2015 PhysioNet/CinC Challenge databases. Some pathological and physiological changes were extracted from the average models of the subjects in different groups. The two-sample ks-test results show that all the parameters between different groups are all markedly different ( $h = 1$ , $p < 0.05$ ). The classification results show that the performances of RF classifiers are better than that of PNN. The kappa coefficients (KC) of RF classifiers are all over 97%, and that of the classifying among bradycardia, tachycardia, and healthy subjects is 98.652 ± 0.217%. Compared with the performance of some former methods, the obtained results demonstrate that the presented method promotes the classification performance remarkably and has the potential to diagnose bradycardia and tachycardia in m-health.