Performance proposed model of training images.

BackgroundHeart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must evaluate electrocardiography (ECG) signals to diagnose MI, which is difficult and prone to observer bias. To be effective in actual practice, an automated, and computerized detection system for Myocardial Infarction using ECG images, must meet a number of criteria.ObjectiveIn an actual clinical situation, these requirements—such as dependability, simplicity, and superior decision-making abilities—remain crucial. In the current work, we have developed a model using a dataset that consists of a combination of 928 ECG images taken from publicly available Mendeley Data. It was converted into three classes Myocardial Infarction, Abnormal heartbeat, and Normal.MethodsThe dataset is then imported, pre-processed, and split into a 70:20:10 ratio of training, validation, and testing. It is then trained using the Siamese Network Model.ResultsThe classification accuracy comes out to be 98%. The algorithm works excellently with datasets having class imbalance by taking pair of images as input. The validation and testing classification matrix is then generated and the evaluation metrics for both of them come out to be a near-perfect value.ConclusionIn this study, we developed the ECG signals based early detection of cardiovascular diseases with Siamese network model.**研究背景** 心肌梗死（myocardial infarction, MI）所致的心肌损伤由血液灌注不足引发。心肌梗死是全球中老年人群的首要致死病因，且因无明显临床症状而难以诊断。临床医师需通过分析心电图（electrocardiography, ECG）信号完成心肌梗死的诊断，但该过程难度较高且易受观察者偏倚影响。若要在实际临床场景中发挥效用，基于心电图图像的心肌梗死自动化计算机辅助检测系统需满足多项严苛标准。 **研究目的** 在真实临床场景中，可靠性、便捷性与卓越的决策能力等核心要求依旧至关重要。本研究采用公开数据集Mendeley Data中的928张心电图图像构建数据集，并将其划分为心肌梗死、异常心律与正常三类，据此开发了检测模型。 **研究方法** 随后对该数据集进行导入、预处理，并按照70:20:10的比例划分为训练集、验证集与测试集，采用孪生网络（Siamese Network）模型开展训练。 **研究结果** 本模型的分类准确率达98%。该算法以图像对作为输入，在类别不平衡数据集上表现优异。随后生成验证集与测试集的分类混淆矩阵，二者的各项评估指标均趋近于完美值。 **研究结论** 本研究采用孪生网络模型，实现了基于心电图信号的心血管疾病早期检测。