心电图心律失常和心肌梗死的心跳分类。
Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.
机构信息
Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan.
Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan.
出版信息
Sensors (Basel). 2023 Mar 9;23(6):2993. doi: 10.3390/s23062993.
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.
心电图（ECG）是评估心脏疾病的基本且快速的检查方法，对于远程患者监护设备至关重要。准确的心电图信号分类对于临床数据的实时测量、分析、归档和传输至关重要。许多研究都集中在准确的心跳分类上，并且提出了深度神经网络以提高准确性和简单性。我们研究了一种新的心电图心跳分类模型，发现它超越了现有技术模型，在 Physionet MIT-BIH 数据集上达到了惊人的 98.5%的准确率，在 PTB 数据库上达到了 98.28%。此外，我们的模型在 PhysioNet Challenge 2017 数据集上的 F1 得分为约 86.71%，表现优于 MINA、CRNN 和 EXpertRF 等其他模型。
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