An improved ResNet-18 model achieved 96.50% accuracy for ECG heartbeat classification on the MIT-BIH arrhythmia database, with 93.83% sensitivity and 97.44% precision for ventricular ectopic beats.
Does an improved ResNet-18 model improve heartbeat classification accuracy of ECG signals compared to other state-of-the-art models?
An improved ResNet-18 convolutional neural network model demonstrates high accuracy (96.50%) for ECG heartbeat classification, particularly for ventricular ectopic beats.
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.
Jing et al. (2021) studied Arrhythmia. Improved ResNet-18 model vs. Other state-of-the-art classification models was evaluated on Classification accuracy. An improved ResNet-18 model achieved 96.50% accuracy for ECG heartbeat classification on the MIT-BIH arrhythmia database, with 93.83% sensitivity and 97.44% precision for ventricular ectopic beats.