Talukdar Debjyoti, de Deus Luis Felipe, Sehgal Nikhil
Medical Research, Mkhitar Gosh Armenian-Russian International University, Yerevan, ARM.
AI Research, Vastmindz Limited, London, GBR.
Cureus. 2022 Jul 14;14(7):e26871. doi: 10.7759/cureus.26871. eCollection 2022 Jul.
Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect many kinds of chronic conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 32% of all deaths worldwide are from CVDs. In addition, stress-related illnesses cost $190 billion in healthcare costs per year. Currently, contact devices are required to extract most of an individual's physiological information, which can be uncomfortable for users and can cause discomfort. However, in recent years, remote photoplethysmography (rPPG) technology is gaining interest, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper, we propose a benchmark comparison using a new multimodal database consisting of 56 subjects where each subject was submitted to three different tasks. Each subject wore a wearable device capable of extracting photoplethysmography signals and was filmed to allow simultaneous rPPG signal extraction. Several experiments were conducted, including a comparison between information from contact and remote signals and stress state recognition. Results have shown that in this dataset, rPPG signals were capable of dealing with motion artifacts better than contact PPG sensors and overall had better quality if compared to the signals from the contact sensor. Moreover, the statistical analysis of the variance method had shown that at least two heart-rate variability (HRV) features, NNi 20 and SAMPEN, were capable of differentiating between stress and non-stress states. In addition, three features, inter-beat interval (IBI), NNi 20, and SAMPEN, were capable of differentiating between tasks relating to different levels of difficulty. Furthermore, using machine learning to classify a "stressed" or "unstressed" state, the models were able to achieve an accuracy score of 83.11%.
定期监测包括心率、血压和血氧饱和度在内的常见生理体征，可能是预防或检测多种慢性病的有效方法。特别是心血管疾病（CVD）是全球关注的问题。根据世界卫生组织的数据，全球32%的死亡是由心血管疾病导致的。此外，与压力相关的疾病每年在医疗保健费用上花费1900亿美元。目前，提取个人大部分生理信息需要使用接触式设备，这可能会让用户感到不适。然而，近年来，远程光电容积脉搏波描记术（rPPG）技术受到关注，它能够使用普通相机对血容量脉搏信号进行非接触式监测，并最终提供与接触式设备相同的生理信息。在本文中，我们提出了一项基准比较，使用一个由56名受试者组成的新多模态数据库，其中每个受试者都要完成三项不同任务。每个受试者都佩戴了一个能够提取光电容积脉搏波信号的可穿戴设备，并进行拍摄以同时提取rPPG信号。进行了多项实验，包括接触式信号与远程信号信息的比较以及压力状态识别。结果表明，在该数据集中，rPPG信号比接触式PPG传感器更能处理运动伪影，并且与接触式传感器的信号相比，总体质量更好。此外，方差方法的统计分析表明，至少有两个心率变异性（HRV）特征，即NNi 20和样本熵（SAMPEN），能够区分压力状态和非压力状态。此外，三个特征，即心跳间期（IBI）、NNi 20和SAMPEN，能够区分与不同难度水平相关的任务。此外，使用机器学习对“有压力”或“无压力”状态进行分类时，模型能够达到83.11%的准确率。
