Heart beat classification and matching recognition based on hierarchical dynamic time warping

Automatic heartbeat classification is an important technique to assist doctors to identify ectopic heartbeats in long-term Holter recording. In this paper, the ECG signal in the MIT-BIH database is filtered first, and then the R-peak detection is performed by the classical method named Pan-Tompkin. The first 100 and the last 150 data points of the R-peak are as chosen as matching signals. Following the recommendation of the Advancement of Medical Instrumentation (AAMI), all the heartbeat samples of MIT-BIH could be grouped into four classes, such as normal or bundle branch block (i.e., class N), supraventricular ectopic (i.e., class S), ventricular ectopic (i.e., class V) and fusion of ventricular and normal (i.e., class F). The division of training and testing data complies with the inter-patient schema. The ECG signals are matched and recognized as specific cardiac diseases using curve fitting and the hierarchical dynamic time warping (DTW) algorithm.Experimental results show that the average classification accuracy of the proposed DTW algorithm is 92.51%, outperforming the other methods. The sensitivities for the classes N, S, V and F are 98.94%, 99.06%, 96.77% and 93.81% respectively, and the corresponding positive predictive values are 93.94%, 91.18%, 88.24% and 96.67%, respectively.