School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China.
Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China.
Physiol Meas. 2024 May 24;45(5). doi: 10.1088/1361-6579/ad46e1.
Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.
心肌梗死（MI）是威胁心血管健康最严重的疾病之一。本文旨在探索一种基于心电图（ECG）自动分类 MI 的算法。
提出了一种融合连续 T 波面积（C_TWA）特征和 ECG 深度特征的 MI 检测方法。该方法主要由三部分组成：（1）MI 的发作通常伴随着 ECG 中 T 波形态的变化，因此不同心跳显示的 T 波面积会有很大差异。采用自适应滑动窗口法检测 T 波的起止点，并在同一 ECG 记录上计算 C_TWA。此外，定义 C_TWA 的变异系数作为 ECG 的 C_TWA 特征。（2）实现多导联融合卷积神经网络提取 ECG 的深度特征。（3）通过软注意力融合 ECG 的 C_TWA 特征和深度特征，然后将其输入多层感知机以获得检测结果。根据患者间范式，该方法在 PTB 数据集上的准确率为 97.67%，精度为 96.59%，召回率为 98.96%，在临床数据集上的准确率为 93.15%，精度为 93.20%，召回率为 95.14%。该方法准确提取了 C_TWA 的特征，并结合了信号的深度特征，从而提高了检测精度，并在临床数据集上取得了良好的效果。
