An efficient ECG modeling for heartbeat classification

In this paper, an efficient heart beat classification algorithm suitable for implementation on mobile devices is presented. A simplified ECG model is used for feature extraction in the time domain. The QRS complex is modeled using straight lines, while P and T waves are modeled using parabolas. The model parameters are estimated by minimizing the root mean square (RMS) of the model error. Heart beats are classified as one of the following: normal (N), supraventricular (S) and Ventricular (V) ectopic beats using a feed-forward neural network. A series of tests have been performed to evaluate the classification algorithm using the MIT-BIH arrhythmia database ECG signals subset and expressed in the terms of sensitivity (Se), specificity (Sp) and accuracy (Acc). The best results were achieved when the classification algorithm was applied on the third model set. The proposed algorithm has been implemented as a J2ME mobile application. It has been tested on signals recorded by a telemedicine health care system and have achieved an average accuracy above 93%.