Cardiac arrhythmia detection using dual-tree wavelet transform and convolutional neural network

The non-stationary ECG signals are used as key tools in screening coronary diseases. ECG recording is collected from millions of cardiac cells and depolarization and re-polarization conducted in a synchronized manner as: the P wave occurs first, followed by the QRS-complex and the T wave, which will repeat in each beat. The signal is altered in a cardiac beat period for different heart conditions. This change can be observed in order to diagnose the patient's heart status. Simple naked eye diagnosis can mislead the detection. At that point, computer-assisted diagnosis (CAD) is therefore required. In this paper dual-tree wavelet transform is used as a feature extraction technique along with deep learning (DL)-based convolution neural network (CNN) to detect abnormal heart. The findings of this research and associated studies are without any cumbersome artificial environments. This work investigates the viability of using deep learning-based architectures for heartbeat classification. The DL architecture is used for the proposed project, and the results suggest that it is feasible to use 2D images to train a deep learning architectures for heartbeat classification. The CNN produced the highest overall accuracy of around 99%. The CAD method proposed has high generalizability; it can help doctors efficiently identify diseases and decrease misdiagnosis.