Wu Qiucen, Lu Xueqi, Wen Yaoqi, Hong Yong, Wu Yuliang, Chen Chaomin
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Tumor Radiotherapy Center, Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan 523059, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2025 Aug 20;45(8):1777-1790. doi: 10.12122/j.issn.1673-4254.2025.08.22.
We propose a myocardial infarction (MI) detection and localization model for improving the diagnostic accuracy for MI to provide assistance to clinical decision-making.
The proposed model was constructed based on multi-scale field residual blocks fusion modified channel attention (MSF-RB-MCA). The model utilizes lead II electrocardiogram (ECG) signals to detect and localize MI, and extracts different levels of feature information through the multi-scale field residual block. A modified channel attention for automatic adjustment of the feature weights was introduced to enhance the model's ability to focus on the MI region, thereby improving the accuracy of MI detection and localization.
A 5-fold cross-validation test of the model was performed using the publicly available Physikalisch-Technische Bundesanstalt (PTB) dataset. For MI detection, the model achieved an accuracy of 99.96% on the test set with a specificity of 99.84% and a sensitivity of 99.99%. For MI localization, the accuracy, specificity and sensitivity were 99.81%, 99.98% and 99.65%, respectively. The performances of the model for MI detection and localization were superior to those of other comparison models.
The proposed MSF-RB-MCA model shows excellent performance in AI detection and localization based on lead II ECG signals, demonstrating its great potential for application in wearable devices.
我们提出一种心肌梗死（MI）检测与定位模型，以提高MI的诊断准确性，为临床决策提供辅助。
所提出的模型基于多尺度场残差块融合改进通道注意力（MSF-RB-MCA）构建。该模型利用II导联心电图（ECG）信号来检测和定位MI，并通过多尺度场残差块提取不同层次的特征信息。引入一种用于自动调整特征权重的改进通道注意力，以增强模型聚焦于MI区域的能力，从而提高MI检测和定位的准确性。
使用公开可用的德国物理技术研究院（PTB）数据集对该模型进行了5折交叉验证测试。对于MI检测，该模型在测试集上的准确率达到99.96%，特异性为99.84%，灵敏度为99.99%。对于MI定位，准确率、特异性和灵敏度分别为99.81%、99.98%和99.65%。该模型在MI检测和定位方面的性能优于其他对比模型。
所提出的MSF-RB-MCA模型在基于II导联ECG信号的人工智能检测和定位中表现出优异性能，证明了其在可穿戴设备中的巨大应用潜力。
