Wavelet-Based Feature Extraction and RCNN for ECG Signal Classification

The proposed method in this paper is an innovative approach for combining Continuous Wavelet Transform (CWT), and Recurrent Convolutional Neural Network (RCNN) to analyze ECG signals. The proposed method's objective is to improve the precision of the ECG signal analysis by integrating the advantages of these two methods. Using CWT, the constituent frequency components of the ECG signals are separated and analyzed, while RCNN is used to classify ECG signals into different classes such as Normal or Abnormal.