Integrated wearable PPG: a multi-vital sign monitoring based on group sparse mode decomposition framework in remote health care using PPG signal.

IF 2.4 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Pankaj, Pratibha Maan, Manjeet Kumar, Ashish Kumar, Rama Komaragiri
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引用次数: 0

Abstract

Monitoring vital signs using a photoplethysmogram (PPG) signal has gained considerable attention, allowing users to monitor anyone, anywhere, and anytime with an objective. In recent years, advances in wearable technology and signal processing techniques have paved the way for accurate and reliable vital sign monitoring using PPG signals. Early detection of cardiovascular diseases can help the physician treat the disease promptly; thus, realtime monitoring of vital signs has emerged. Any deviation in the threshold value of vital signs can indicate potential threats to the cardiovascular system. The need to monitor vital signs in realtime using wearable devices has attracted the interest of the healthcare industry in developing simple and efficient vital sign estimation algorithms. This research introduces a framework to estimate the following important vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), and blood oxygen saturation (SpO2), concurrently by overcoming the limitations posed by state-of-the-art techniques that primarily focus on individual or two vital sign estimations. Our proposed approach leverages signal processing techniques to determine the above-mentioned vital signs seamlessly and accurately. This innovation enhances the efficiency of vital sign monitoring and presents a unified solution for comprehensive health assessment. The widespread use of wearable devices for monitoring realtime health status in everyday life manifests in using PPG sensor-enabled wearable devices to perform more complex computational tasks. To date, the algorithms proposed to process an input PPG signal often use multiple processing steps to estimate any vital signs. This can increase the computational complexity of these algorithms, making it challenging to deploy devices with limited computational resources. The proposed work introduces a computationally efficient framework to estimate all four vital signs using the signal framework. The experimental results obtained with the proposed framework demonstrate that the proposed work outperforms the state-of-the-art estimation accuracy and computational complexity.

集成式可穿戴 PPG:基于组稀疏模式分解框架的多生命体征监测,利用 PPG 信号进行远程医疗保健。
利用光电容积描记图(PPG)信号监测生命体征已经获得了相当大的关注,使用户可以随时随地有目标地监测任何人。近年来,可穿戴技术和信号处理技术的进步为利用PPG信号进行准确可靠的生命体征监测铺平了道路。心血管疾病的早期发现可以帮助医生及时治疗;因此,生命体征的实时监测出现了。生命体征阈值的任何偏差都可能表明心血管系统存在潜在威胁。使用可穿戴设备实时监测生命体征的需求吸引了医疗保健行业对开发简单高效的生命体征估计算法的兴趣。本研究引入了一个框架来估计以下重要的生命体征:心率(HR)、呼吸频率(RR)、血压(BP)和血氧饱和度(SpO2),同时克服了主要关注单个或两个生命体征估计的最先进技术所带来的局限性。我们提出的方法利用信号处理技术来无缝、准确地确定上述生命体征。这一创新提高了生命体征监测的效率,为综合健康评估提供了统一的解决方案。在日常生活中,可穿戴设备广泛用于监测实时健康状况,体现在使用PPG传感器支持的可穿戴设备执行更复杂的计算任务。迄今为止,用于处理输入PPG信号的算法通常使用多个处理步骤来估计任何生命体征。这可能会增加这些算法的计算复杂性,使部署具有有限计算资源的设备变得具有挑战性。提出的工作引入了一个计算效率高的框架,使用信号框架来估计所有四个生命体征。实验结果表明,该框架具有较好的估计精度和计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.40
自引率
4.50%
发文量
110
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