心肌梗塞(MI)是指由于冠状动脉突然阻塞而导致心肌供血不足而对心脏组织造成的损害。这种阻塞通常是脂肪(胆固醇)在动脉中形成斑块(动脉粥样硬化)的积累的结果。随着时间的推移,这些斑块会破裂,导致凝块(血栓)的形成,会阻塞动脉导致心脏病发作.心脏病发作的危险因素包括吸烟,高血压,糖尿病,高胆固醇,代谢综合征,和遗传倾向。MI的早期诊断至关重要。因此,检测和分类MI是必不可少的。本文介绍了一种使用频谱图和贝叶斯优化(MI-CSBO)进行心电图(ECG)的MI分类的新混合方法。首先,使用频谱图方法将来自PTB数据库(PTBDB)的ECG信号从时域转换到频域。然后,将深度残差CNN应用于ECG成像数据的测试和训练数据集。然后获取使用深度残差模型训练的ECG数据集。最后,贝叶斯方法,NCA功能选择,和各种机器学习算法(k-NN,SVM,树,袋装,朴素贝叶斯,合奏)用于得出绩效指标。MI-CSBO方法实现了100%的正确诊断率,如实验结果部分所述。
Myocardial Infarction (MI) refers to damage to the heart tissue caused by an inadequate blood supply to the heart muscle due to a sudden blockage in the coronary arteries. This blockage is often a result of the accumulation of fat (cholesterol) forming plaques (atherosclerosis) in the arteries. Over time, these plaques can crack, leading to the formation of a clot (thrombus), which can block the artery and cause a heart attack. Risk factors for a heart attack include smoking, hypertension, diabetes, high cholesterol, metabolic syndrome, and genetic predisposition. Early diagnosis of MI is crucial. Thus, detecting and classifying MI is essential. This paper introduces a new hybrid approach for MI Classification using Spectrogram and Bayesian Optimization (MI-CSBO) for Electrocardiogram (ECG). First, ECG signals from the PTB Database (PTBDB) were converted from the time domain to the frequency domain using the spectrogram method. Then, a deep residual CNN was applied to the test and train datasets of ECG imaging data. The ECG dataset trained using the Deep Residual model was then acquired. Finally, the Bayesian approach, NCA feature selection, and various machine learning algorithms (k-NN, SVM, Tree, Bagged, Naïve Bayes, Ensemble) were used to derive performance measures. The MI-CSBO method achieved a 100% correct diagnosis rate, as detailed in the Experimental Results section.