Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques

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...

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Bibliographic Details
Published in:Journal of sensors 2022-09, Vol.2022, p.1-11
Main Authors: Pandey, Saroj Kumar, Kumar, Gaurav, Shukla, Shubham, Kumar, Ankit, Singh, Kamred Udham, Mahato, Shambhu
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Cardiac arrhythmia
Cardiology
Classification
Clinical medicine
Data analysis
Datasets
Deep learning
Electrocardiography
Fibrillation
Heart failure
Heart rate
Machine learning
Morphology
Radial basis function
Rhythm
Signal processing
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Summary: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.
ISSN:1687-725X
1687-7268
DOI:10.1155/2022/6732150