Features extraction and classification of ECG beats using CWT combined to RBF neural network optimized by cuckoo search via levy flight

This paper presents a method for classification of normal and abnormal arrhythmia beats using the continuous wavelet transform to extract features and RBF optimized by cuckoo search algorithm via Levy flight. We have optimized the RBF classifier by searching the best values of parameters. The experiments were conducted on the ECG data from the MIT-BIH arrhythmia database to classify abnormal and normal beats, the RBF-CS via Levy flight yielded on overall sensitivity 98.92% and an overall accuracy 98.32 %.