Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 2 2024 03 31 255 264 Shrikanth Rao S.K Department of Electronics & Communication Engineering, Vivekananda College of Engineering & Technology, Puttur, Dakshina Kannada, India MaheshKumar H Kolekar Department of Electrical Engineering, Indian Institute of Technology, Patna, India Roshan Joy Martis Department of Computer Science and Engineering, Global Academy of Technology, Bangalore, India 2022 09 27 2022 11 24 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. https://fbt.tums.ac.ir/index.php/fbt/article/view/555 https://fbt.tums.ac.ir/index.php/fbt/article/download/555/399