A Deep Learning Approach for Detecting Atrial Fibrillation using RR Intervals of ECG

Shrikanth  Rao S.K; MaheshKumar  H Kolekar; Roshan Joy  Martis

doi:10.18502/fbt.v11i2.15343

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

DOI: https://doi.org/10.18502/fbt.v11i2.15343

Keywords: Atrial Fibrillation; Electrocardiogram; Discrete Wavelet Transform; Savitzky-Golay Filter; Convolutional Neural Network; Long Short Term Memory; ResNet18.

Abstract

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.