Issa Mohamed F, Yousry Ahmed, Tuboly Gergely, Juhasz Zoltan, AbuEl-Atta Ahmed H, Selim Mazen M
Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13511, Egypt.
Department of Electrical Engineering and Information Systems, University of Pannonia, 8200, Veszprém, Hungary.
Heliyon. 2023 Jul 5;9(7):e17974. doi: 10.1016/j.heliyon.2023.e17974. eCollection 2023 Jul.
The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.
心电图（ECG）信号的分析与处理是心血管疾病诊断中的关键步骤。心电图提供了一种无创且无风险的方法来监测心脏的电活动，有助于预测和诊断心脏病。然而，即使对于专家而言，手动解读心电图信号也可能具有挑战性且耗时。机器学习技术正越来越多地用于支持自动心电图分类的研究与开发，这已成为一个突出的研究领域。在本文中，我们提出了一种带有残差块的深度神经网络模型（DNN-RB），用于将心动周期分类为六种心电图搏动类别。使用麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH）数据集对该模型进行验证，测试准确率达到99.51%，平均灵敏度为99.7%，平均特异性为98.2%。DNN-RB方法在同一数据集上测试时，比其他现有最先进算法取得了更高的准确率。所提出的方法在心电图信号的自动分类中是有效的，并且可与单导联移动心电图设备结合用于临床和院外监测及分类。该方法还已集成到一个网络应用程序中，该应用程序旨在接受数字心电图搏动作为分析输入并显示诊断结果。
