Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi Arabia.
Sensors (Basel). 2023 Jul 26;23(15):6675. doi: 10.3390/s23156675.
Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors can be combined as numerous clinical devices to store the biosignals generated by the physiological actions of the human body. Meanwhile, a familiar method with a noninvasive and rapid biomedical electrocardiogram (ECG) signal can be used to diagnose and examine cardiovascular disease (CVD). As the growing number of patients is destroying the classification outcome because of major changes in the ECG signal patterns among numerous patients, computer-assisted automatic diagnostic tools are needed for ECG signal classification. Therefore, this study presents a mud ring optimization technique with a deep learning-based ECG signal classification (MROA-DLECGSC) technique. The presented MROA-DLECGSC approach recognizes the presence of heart disease using ECG signals. To accomplish this, the MROA-DLECGSC technique initially preprocessed the ECG signals to transform them into a uniform format. In addition, the Stacked Autoencoder Topographic Map (SAETM) approach was utilized for the classification of ECG signals to identify the presence of CVDs. Finally, the MROA was applied as a hyperparameter optimizer, which assisted in accomplishing enhanced performance. The experimental outcomes of the MROA-DLECGSC algorithm were tested on the benchmark database, and the results show the better performance of the MROA-DLECGSC methodology compared to other recent algorithms.
由于物联网 (IoT)、感测技术和可穿戴设备的巨大发展，医疗服务的质量得到了提高，并且已经从基于标准的医疗健康服务转变为实时服务。通常，可以将传感器组合成许多临床设备，以存储人体生理活动产生的生物信号。同时，可以使用一种常见的方法，即无创且快速的生物医学心电图 (ECG) 信号，来诊断和检查心血管疾病 (CVD)。由于大量患者的 ECG 信号模式发生重大变化，破坏了分类结果，因此需要计算机辅助的自动诊断工具来对 ECG 信号进行分类。因此，本研究提出了一种基于 mud ring optimization algorithm 和深度学习的 ECG 信号分类方法 (MROA-DLECGSC)。所提出的 MROA-DLECGSC 方法使用 ECG 信号来识别心脏病的存在。为此，MROA-DLECGSC 技术首先对 ECG 信号进行预处理，将其转换为统一的格式。此外，还使用了堆叠自动编码器拓扑图 (SAETM) 方法对 ECG 信号进行分类，以识别 CVD 的存在。最后，应用 MROA 作为超参数优化器，以帮助实现更好的性能。在基准数据库上测试了 MROA-DLECGSC 算法的实验结果，结果表明，与其他最近的算法相比，MROA-DLECGSC 方法具有更好的性能。
