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

被引：0

作者：

Pandey, Saroj Kumar ^{[1
]}

Kumar, Gaurav ^{[1
]}

Shukla, Shubham ^{[2
]}

Kumar, Ankit ^{[1
]}

Singh, Kamred Udham ^{[3
]}

Mahato, Shambhu ^{[4
]}

机构：

[1] Department of Computer Engineering & Applications, GLA University, Mathura,281406, India

[2] KIET Group of Institutions, Ghaziabad,201001, India

[3] Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan,701, Taiwan

[4] Department of Education, Janajyoti Multiple Campus, Sarlahi, Lalbandi, Nepal

来源：

Journal of Sensors | 2022年 / 2022卷

关键词：

Convolution - Convolutional neural networks - Deep neural networks - Diseases - Learning algorithms - Radial basis function networks;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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. © 2022 Saroj Kumar Pandey et al.

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