ECG beat classification by using discrete wavelet transform and Random Forest algorithm

Until now, there has been no study in the literature that uses Random Forest algorithm for the classification of ECG beats. In this study, the ECG signals obtained from the MIT/BIH database were used to classify the five heartbeat classes (N, L, R, V, P). Feature extraction from the ECG signals for classification of ECG beats was performed by using discrete wavelet transform (DWT). The Random Forest was then presented for the classification of the ECG signals. Five types of ECG beats were classified with a success of 99.8%. Since Random Forest algorithm works very fast, gives excellent performance and there is no cross validation, it can be useful for long-term ECG beat classification.