a deep learning approach for detecting atrial fibrillation using rr intervals of ecg

Purpose: atrial fibrillation (af) is one of the most common types of heart arrhythmias observed in clinical practice. af can be detected using an electrocardiogram (ecg). ecg signals are time-varying and nonlinear in nature. hence, it is very difficult for a physician to manually perform accurate and rapid classification of different heart rhythms.materials and methods: in this paper, we propose a method using discrete wavelet transform (dwt) with db6 as the basis function for denoising ecg signal.results: the denoised ecg is smoothened using the savitzky- golay filter. deep learning methods, such as a combination of convolutional neural network (cnn) and long short term memory (lstm) (cnn-lstm) and resnet18 are used for the accurate classification of ecg signals using physionet challenge 2017 database.conclusion: with a 10-fold cross-validation method the model provided overall accuracy of 98.25% with the cnn-lstm classifier.