Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt.
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Sensors (Basel). 2022 Aug 29;22(17):6503. doi: 10.3390/s22176503.
An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.
心电图（ECG）是一种至关重要的医疗设备，可帮助诊断患者的各种心脏相关疾病。需要一种自动化诊断工具来检测长期 ECG 记录中的重要事件。由于心脏病专家需要在短时间内分析长期 ECG 记录，因此需要基于计算机的诊断工具来识别关键事件。心肌梗死（MI）和传导障碍（CDs），有时也称为心脏阻滞，是当冠状动脉完全或突然停止或这些动脉中的血液流动明显减慢时发生的医学疾病。因此，许多研究人员已经使用深度学习方法来检测 MI 和 CD。然而，在使用深度学习算法时，存在以下一个或多个挑战：（i）在实际数据中存在困难，（ii）训练阶段之后的时间也需要高处理能力，（iii）它们非常昂贵，需要大量的内存和计算资源，并且不容易将它们转移到其他问题，（iv）它们难以描述，并且不完全理解（黑盒），以及（v）大多数文献都基于 MIT-BIH 或 PTB 数据库，这些数据库没有涵盖大多数关键的心律失常。本文提出了一种新的基于机器学习的深度学习方法，用于使用大型 PTB-XL ECG 数据检测 MI 和 CDs。首先，考虑了这些心脏信号的所有挑战问题，因为信号数据来自不同的数据集并且对数据进行了滤波。之后，将 MI 和 CD 信号输入到深度学习模型中以提取深度特征。此外，提出了一种新的自定义激活函数，该函数与常规激活函数相比具有更快的收敛速度。之后，将这些特征输入到外部分类器（例如支持向量机（SVM））中进行检测。实验结果证明了所提出方法的效率，当使用 CNN 提取特征并使用 SVM 分类器时，它的整体准确率提高到了 99.20%，这表明它有了明显的改善。
