RECOGNITION OF ATRIAL FIBRILLATION BASED ON CNN-LSTM AND LAPLACIAN SUPPORT VECTOR MACHINE

• Published in: Journal of mechanics in medicine and biology Vol. 24; no. 4
• Main Authors: WANG, YING, LI, YONGJIAN, CHEN, MENG, HUO, RUI, LIU, LEI, LIANG, YESONG, WEI, SHOUSHUI
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.05.2024; World Scientific Publishing Co. Pte., Ltd
• Subjects: Accuracy; Artificial neural networks; Cardiac arrhythmia; Deep learning; Feature extraction; Fibrillation; Machine learning; Research Article; Semi-supervised learning; Support vector machines; Laplacian support vector machine; electrocardiogram; semi-supervised learning; CNN-LSTM; Atrial fibrillation detection
• ISSN: 0219-5194; 1793-6810
• DOI: 10.1142/S0219519423500914

Atrial fibrillation (AF) is a common arrhythmia associated with cardiac death and stroke. Therefore, the early detection of AF is of critical importance and the wearable long-term ECG monitoring system is one promising way. In order to assist the cardiologists in identifying potential AF in a tremendous amount of long-term ECG data, this study proposed an automatic detector combining deep learning and semi-supervised learning in view of the difficulty of obtaining a large number of labeled data in clinical practice. Three R-R interval features and two nonlinear features extracted from ECG samples, combined with 16-dimension deep learning features extracted by CNN-LSTM, are put into the semi-supervised machine learning model Laplacian Support Vector Machine (LapSVM) for AF detection. The proposed method showed very promising performance, with an accuracy of 99.63%, a sensitivity of 99.70%, a specificity of 99.57% and an F_score of 99.59% on the AFDB dataset. It still achieved an accuracy of 98% when the proportion of the training set was reduced, and achieved an accuracy of 96% on the SPHD collected clinically. The results show that the proposed method can classify AF and non-AF with a higher accuracy, and has excellent generalization performance in different categories of subjects, which is in line with clinical scenarios. The proposed method is also conducive to solving the clinical cases with little labeled data.