Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques. (22nd September 2022)

• Record Type: Journal Article
• Authors: Pandey, Saroj Kumar; Kumar, Gaurav; Shukla, Shubham; Kumar, Ankit; Singh, Kamred Udham; Mahato, Shambhu
• Other Names: Yin Aijun Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-09-22
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/6732150
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : In cardiac rhythm disorders, atrial fibrillation (AF) is among the most deadly. So, ECG signals play a crucial role in preventing CVD by promptly detecting atrial fibrillation in a patient. Unfortunately, locating trustworthy automatic AF in clinical settings remains difficult. Today, deep learning is a potent tool for complex data analysis since it requires little pre and postprocessing. As a result, several machine learning and deep learning approaches have recently been applied to ECG data to diagnose AF automatically. This study analyses electrocardiogram (ECG) data from the PhysioNet/Computing in Cardiology (CinC) Challenge 2017 to differentiate between atrial fibrillation (AF) and three other rhythms: normal, other, and too noisy for assessment. The ECG data, including AF rhythm, was classified using a novel model based on a combination of traditional machine learning techniques and deep neural networks. To categorize AF rhythms from ECG data, this hybrid model combined a convolutional neural network (Residual Network (ResNet)) with a Bidirectional Long Short Term Memory (BLSTM) network and a Radial Basis Function (RBF) neural network. Both the F1-score and the accuracy of the final hybrid model are relatively high, coming in at 0.80% and 0.85%, respectively.