Automatic Cardiac Arrhythmia Classification Using Residual Network Combined With Long Short-Term Memory

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 17
• Main Authors: Kim, Yun Kwan, Lee, Minji, Song, Hee Seok, Lee, Seong-Whan
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrhythmia; Arrhythmia classification; Atrial fibrillation; augmentation; Cardiac arrhythmia; Cardiology; Classification; Deep learning; Diagnosis; Electrocardiography; electrocardiography (ECG); Feature extraction; few shot; Fibrillation; long short-term memory; Machine learning; Picture archiving and communication systems; residual network (ResNet); Rhythm; squeeze-and-excitation (SE) block
• DOI: 10.1109/TIM.2022.3181276
• ISSN: 0018-9456

Diagnosis and classification of arrhythmia, which is associated with abnormal electrical activities in the heart, are critical for clinical treatments. Previous studies focused on the diagnosis of atrial fibrillation, which is the most common arrhythmia in adults. The classification performance achieved by studies on other arrhythmia types is not satisfactory for clinical use owing to the small number of classes (minority classes). In this study, we propose a novel framework for automatic classification that combines a residual network with a squeeze-and-excitation block and a bidirectional long short-term memory. Eight-, four-, and two-class performances were evaluated on the MIT-BIH arrhythmia database (MITDB), the MIT-BIH atrial fibrillation database (AFDB), and the PhysioNet/Computing in the cardiology challenge 2017 database (CinC DB), respectively, and they were superior to the performance achieved by conventional methods. In addition, the classwise F1-score in the minority classes was higher than those of the methods adopted in existing studies. To measure the generalization ability of the proposed framework, AFDB and CinC DB were tested using an MITDB-trained model, and superior performance was achieved compared with ShallowConvNet and DeepConvNet. We performed a cross-subject experiment using AFDB and obtained a statistically higher performance using the proposed method compared with typical machine learning methods. The proposed framework can enable the direct diagnosis of arrhythmia types in clinical trials based on the accurate detection of the minority class.