ECG beat classification via deterministic learning

This paper proposes a novel method for the electrocardiographic (ECG) beat classification via deterministic learning. The dynamics of ECG beats is used as a unique feature for ECG beat classification, which is fundamentally different from the time/frequency domain features used in literature. It is the essential feature of ECG beats, and contains complete information of ECG beats. Precisely, the deterministic learning allows us to model and represent the dynamics of a training beat set as constant radial basis function (RBF) networks. As the classification measure, a set of errors is further obtained through the comparison between the test beat and the estimators constructed by the RBF networks. ECG records taken from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database are selected to test the proposed method. With 5% beats used as training beats, the overall accuracies are 97.78% and 97.21% for global and patient-adapting beat classification, respectively. These results indicate the proposed method is reliable and efficient for ECG beat classification.