Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain.
Sensors (Basel). 2022 Apr 9;22(8):2900. doi: 10.3390/s22082900.
Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients' health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. This system can be used in a static and/or ambulatory way, providing information about the health state through features extracted from the ear EEG signal and the calculation of the heart rate variability (HRV) and pulse travel time (PTT). The different applied processing techniques to improve the quality of these signals are described in this work. A novel algorithm used to compute HRV and PTT robustly and accurately in ambulatory settings is also described. The developed device has also been validated and compared with other commercial systems obtaining similar results. In this way, based on the quality of the obtained signals and the low variability of the computed parameters, even in ambulatory conditions, the developed device can potentially serve as a support tool for clinical decision-taking stages.
癫痫是一种具有重大社会影响的慢性疾病,因为患者及其家属的生活常常受到癫痫发作及其可能后果(如事故、伤害,甚至突发的不明原因死亡)的影响。在此背景下,移动监测可以收集有关患者健康的生物医学数据,从而更深入地了解每位患者的生理状态和日常活动。出于这个原因,本文提出了一种由不同传感器组成的新型监测系统,该系统能够同步记录心电图(ECG)、光电容积脉搏波(PPG)和耳脑电图(EEG)信号,并将其存储在 microSD 卡中,以备进一步处理和分析。该系统可以用于静态和/或移动监测,通过从耳 EEG 信号中提取的特征和心率变异性(HRV)和脉搏传输时间(PTT)的计算,提供有关健康状态的信息。本文还描述了为提高这些信号质量而应用的不同处理技术。还描述了一种用于在移动环境中稳健且准确地计算 HRV 和 PTT 的新型算法。所开发的设备也已经过验证,并与其他商业系统进行了比较,结果相似。这样,基于获得的信号质量和计算参数的低可变性,即使在移动条件下,所开发的设备也有可能成为临床决策阶段的支持工具。