Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan.
Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
Sensors (Basel). 2023 Sep 6;23(18):7697. doi: 10.3390/s23187697.
Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and widespread availability for CVD management. In this research, we proposed an IoT-based monitoring and detection system for cardiac patients, employing a two-stage approach. In the initial stage, we used a routing protocol that combines routing by energy and link quality (REL) with dynamic source routing (DSR) to efficiently collect data on an IoT healthcare platform. The second stage involves the classification of ECG images using hybrid-based deep features. Our classification system utilizes the "ECG Images dataset of Cardiac Patients", comprising 12-lead ECG images with four distinct categories: abnormal heartbeat, myocardial infarction (MI), previous history of MI, and normal ECG. For feature extraction, we employed a lightweight CNN, which automatically extracts relevant ECG features. These features were further optimized through an attention module, which is the method's main focus. The model achieved a remarkable accuracy of 98.39%. Our findings suggest that this system can effectively aid in the identification of cardiac disorders. The proposed approach combines IoT, deep learning, and efficient routing protocols, showcasing its potential for improving CVD diagnosis and management.
心脏疾病是全球死亡的主要原因之一，这强调了对心血管疾病（CVD）进行初始诊断和预防的必要性。心电图（ECG）程序是非常推荐的，因为它提供了关键的心脏病学信息。远程医疗为 CVD 管理提供了低成本工具和广泛可用性的机会。在这项研究中，我们提出了一种基于物联网的心脏病人监测和检测系统，采用两阶段方法。在初始阶段，我们使用了一种路由协议，该协议将能量和链路质量（REL）路由与动态源路由（DSR）相结合，以便在物联网医疗保健平台上高效地收集数据。第二阶段涉及使用混合深度特征对 ECG 图像进行分类。我们的分类系统利用包含 12 导联 ECG 图像的“心脏病人 ECG 图像数据集”，这些图像分为四个不同类别：异常心跳、心肌梗死（MI）、MI 病史和正常 ECG。对于特征提取，我们使用了一个轻量级 CNN，它可以自动提取相关的 ECG 特征。这些特征通过注意力模块进一步优化，这是该方法的主要关注点。该模型的准确率达到了 98.39%。我们的研究结果表明，该系统可以有效地帮助识别心脏疾病。所提出的方法结合了物联网、深度学习和高效路由协议，展示了其在改善 CVD 诊断和管理方面的潜力。
