多模式生理信号监测的机器辅助PPG信号质量评估(SQA)

Win-Ken Beh, Yu-Chia Yang, Yi-Cheng Lo, Yun-Chieh Lee, An-Yeu Wu
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引用次数: 0

摘要

光容积脉搏波描记术(PPG)是一种记录人体生命体征的无创技术。PPG通常由易于产生伪影的可穿戴设备记录。这将导致信号损坏,从而降低测量精度。因此,信号质量评估(SQA)系统对于获得可靠的测量是必不可少的。传统上,SQA主要由人类知识驱动,并通过专家的注释进行监督。然而,它们并没有针对领域应用程序的特殊性进行定制。因此,我们提出了一个机器辅助的SQA框架,为应用程序生成相应的质量标准。通过使用所提出的方法,可以很容易地为不同的应用程序训练质量标准。然后,将质量评价应用于几种基于ppg的生理信号远程监测。与传统方法相比,该系统对高误差信号的拒绝率更高,并且在估计心率(-3.06 BPM)、确定呼吸频率(-1.36 BPM)和预测高血压(+24%)时实现了更低的平均绝对误差。提出的方法提高了监测生理信号的准确性,因此适合医疗保健应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine-aided PPG Signal Quality Assessment (SQA) for Multi-mode Physiological Signal Monitoring
Photoplethysmography (PPG) is a non-invasive technique for recording human vital signs. PPG is normally recorded by wearable devices that are prone to artifacts. This results in signal corruption that decreases measurement accuracy. Thus, a signal quality assessment (SQA) system is essential in obtaining reliable measurements. Conventionally, SQA is mainly driven by human-knowledge and supervised through experts’ annotations. However, they are not tailored for the particularities of the domain applications. Hence, we propose a machine-aided SQA framework that generates respective quality criteria for applications. By using the proposed approach, quality criteria can be easily trained for different applications. Then, quality assessment can be applied to several PPG-based physiological signals telemonitoring. Compared with conventional approaches, the proposed system has a higher rejection rate for high-error signals and a lower mean absolute error is achieved when estimating heart rate (-3.06 BPM), determining respiration rate (–1.36 BPM), and predicting hypertension (+24%). The proposed method enhances accuracy in monitoring physiological signals and thus is suitable for healthcare applications.
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CiteScore
10.30
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