Alsheikhy Ahmed, Said Yahia F, Shawly Tawfeeq, Lahza Husam
Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia.
Department of Electrical Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Healthcare (Basel). 2023 Jan 22;11(3):330. doi: 10.3390/healthcare11030330.
ECG provides critical information in a waveform about the heart's condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians.
心电图以波形形式提供有关心脏状况的关键信息。该信息对医生至关重要，因为它是心脏病专家首先要进行的检查项目。当新冠疫情在全球蔓延并成为大流行时，沙特阿拉伯政府实施了各种限制措施和指导方针以保护和拯救公民及居民。其中一项限制措施是禁止个人触摸公共场所和私人场所的任何表面。此外，当局在所有公共设施和私营部门实施了一项强制性规定，要求在人员进入前测量体温。因此，本研究的想法源于需要一种非接触式技术来确定心率。本文提出了一种可行且可靠的方法，基于皮肤上的反射光来估计平均心率。该模型使用了各种深度学习工具，包括AlexNet、卷积神经网络（CNN）、长短期记忆网络（LSTM）和ResNet50V2。已经进行了三种场景的测试来评估和验证所提出的模型。此外，所提出的方法从视频流中获取输入，并将这些流转换为帧和图像。已经对志愿者进行了多次试验，以验证该方法并评估其在准确性、平均绝对误差（MAE）和均方误差（MSE）方面的输出。所提出的模型平均准确率达到99.78%，当结合LSTM和ResNet50V2时，MAE为0.142，而MSE为1.82。此外，还对所提出的算法与文献中的一些研究基于所使用的方法、MAE和MSE进行了比较测量。所取得的结果表明，所开发的技术优于其他方法。此外，研究结果表明该算法可应用于医疗保健设施并帮助医生。
