Automatic heartbeat classification using S-shaped reconstruction and a squeeze-and-excitation residual network. (January 2022)

• Record Type: Journal Article
• Authors: Li, Xiangyu; Zhang, Feng; Sun, Zhenjuan; Li, Dong; Kong, Xiaoming; Zhang, Yatao
• Is Part Of: Computers in biology and medicine. Volume 140(2022)
• Publication Date: 2022-01
• Subjects: Arrhythmia heartbeat -- S- shaped reconstruction -- SE-ResNet; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105108
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: To facilitate the identification of arrhythmia, in this study, an S-shaped reconstruction method was proposed, and a two-dimensional (2-D) 19-layer deep squeeze-and-excitation residual network (SE-ResNet) was used to classify heartbeats. The proposed method has three steps. The first step involves data preprocessing, which includes denoising of the original electrocardiogram (ECG) data, removing of baseline drift, heartbeat extraction, and data balancing using a synthetic minority oversampling technique algorithm. Subsequently, the extracted one-dimensional heartbeat series is transformed into a 2-D matrix by employing the novel S-shaped reconstruction method for determining the relationship between distant points in an ECG series. Finally, the 2-D 19-layer SE-ResNet is used to divide the 2-D heartbeat matrix into five heartbeat categories, namely normal, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats, in accordance with the American National Standards Institute/Advancement of Medical Instrumentation standard, and 10-fold cross-validation is employed to train the 2-D 19-layer SE-ResNet. The accuracy, positive prediction rate, sensitivity, and specificity of the proposed method reached 99.61%, 93.87%, 93.78%, and 99.27%, respectively. The results indicated that the S-shaped reconstruction method can be helpful for acquiring additional information from ECG heartbeat data. Highlights: Arrhythmia detection and classification using deep learning model. S-shaped reconstruction is used to easily convert the ECG heartbeat into a 2-D matrix. Using SE-ResNet to establish the predictive model. Two data partition ways are used to train and test the model, and the results are compared. 10-fold cross-validation is introduced to evaluate the performance of the model.