ECG autoencoder based on low-rank attention

• Published in: Scientific reports Vol. 14; no. 1; pp. 12823 - 10
• Main Authors: Zhang, Shilin, Fang, Yixian, Ren, Yuwei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 692/699/75; Algorithms; Arrhythmia; Arrhythmias, Cardiac - diagnosis; Arrhythmias, Cardiac - physiopathology; Cardiac arrhythmia; Cardiovascular diseases; Cardiovascular Diseases - diagnosis; EKG; Electrocardiography; Electrocardiography - methods; Humanities and Social Sciences; Humans; Machine Learning; multidisciplinary; Neural networks; Neural Networks, Computer; Performance assessment; Science; Science (multidisciplinary); Signal Processing, Computer-Assisted
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-63378-0

The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gaining prominence in the field of machine learning. However, prevailing neural network models frequently disregard the spatial dimension features inherent in ECG signals. In this paper, we propose an ECG autoencoder network architecture incorporating low-rank attention (LRA-autoencoder). It is designed to capture potential spatial features of ECG signals by interpreting the signals from a spatial perspective and extracting correlations between different signal points. Additionally, the low-rank attention block (LRA-block) obtains spatial features of electrocardiogram signals through singular value decomposition, and then assigns these spatial features as weights to the electrocardiogram signals, thereby enhancing the differentiation of features among different categories. Finally, we utilize the ResNet-18 network classifier to assess the performance of the LRA-autoencoder on both the MIT-BIH Arrhythmia and PhysioNet Challenge 2017 datasets. The experimental results reveal that the proposed method demonstrates superior classification performance. The mean accuracy on the MIT-BIH Arrhythmia dataset is as high as 0.997, and the mean accuracy and F 1 -score on the PhysioNet Challenge 2017 dataset are 0.850 and 0.843.