Recognition of ECG signals using wavelet based on atomic functions

Heart disease is the principal cause of death across the globe and the ECG signals are used to diagnose it. The correct classification of this disease allows us the opportunity to apply a more focused treatment. ECG signals are fed into Automated Diagnosis Systems, and these systems use techniques like processing digital signals, machine learning, and deep learning. This paper shows the results when the sampling frequency of the ECG signals is resampled and proposes a new preprocessing stage. The new stage applies Wavelet based on Atomic Functions to eliminate the noise and baseline wander. The Wavelet based on Atomic Functions have demonstrated successful performances in computer science. The ECG signals are segmented into 1, 2, 5, and 10 s; these segmented signals are fed into the classifier stage. Our proposal was tested in four accessible public databases separately, and finally by gathering the databases. We were able to successfully differentiate between 11 types of ECG signals with an accuracy of 98.9%.