Semi-supervised learning for ECG classification without patient-specific labeled data

• Published in: Expert systems with applications Vol. 158; p. 113411
• Main Authors: Zhai, Xiaolong, Zhou, Zhanhong, Tin, Chung
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.11.2020; Elsevier BV
• Subjects: Algorithms; Arrhythmia; Artificial neural networks; Batch processing; Classification; CNN; ECG classification; Labeling; Labels; Modules; Semi-supervised learning; Time series signal; CNN; Time series signal; Arrhythmia; Semi-supervised learning; ECG classification
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113411

•A high performance semi-supervised ECG classification system is presented.•Including some patient-specific N beats for training is a practical and effective way to improve performance.•An unsupervised method is designed to estimate these patient-specific N beats for training.•A semi-supervised iterative label update method is designed to further improve the ECG classification performance.•Sen and Spe for S and V beats prediction are comparable with several supervised methods. In this paper, we propose a semi-supervised learning-based ECG classification system for detection of supraventricular ectopic beats (SVEB or S beats) and ventricular ectopic beats (VEB or V beats) which does not require manual labeling of the patient-specific ECG data. Owing to inter-subject variability in ECG signal, patient-specific data is usually required to achieve good performance in ECG classification system. However, manual labeling of patient-specific data requires expert intervention, which is costly and time consuming. Our proposed system is based on a 2D convolutional neural network (CNN) with inputs generated from heartbeat triplets. The system also consists of two auxiliary modules: a normal beat estimation module and an iterative beat label update algorithm. The normal beat estimation selects a small amount of patient-specific normal beats accurately from the testing ECG record in an unsupervised manner. These estimated normal beats are used, together with a common pool dataset, to train a preliminary patient-specific CNN classifier which provides initial labels for the testing data. These labels then undergo a semi-supervised iterative update process for improved performance. Our proposed system was evaluated on the MIT-BIH arrhythmia database. The training of our proposed system is fully automatic, and its performance is comparable with several state-of-art supervised methods which require extra manual labeling of patient-specific ECG data. Our proposed system can be a useful tool for batch processing a large amount of ECG data in clinical applications.