Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in‐hospital or in‐home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high‐quality, marginal‐quality, or poor‐quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good‐quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good‐quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good‐, marginal‐, and poor‐quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist‐worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.

中文翻译：

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用于癫痫管理的可穿戴设备的信号质量和患者体验

无创可穿戴设备在帮助癫痫管理方面具有巨大潜力，但这些设备必须具有强大的信号质量，并且患者必须愿意长时间佩戴它们。可穿戴生物传感器信号的自动机器学习分类需要对信号质量进行定量测量，以自动拒绝质量差或损坏的数据段。在这项研究中，市售的可穿戴传感器被放置在接受院内或家庭脑电图 (EEG) 监测的癫痫患者和健康志愿者身上。Empatica E4 和 Biovotion Everion 用于记录加速度计 (ACC)、光电容积描记 (PPG) 和皮肤电活动 (EDA)。Byteflies Sensor Dots 用于记录 ACC 和 PPG，Activinsights GENEActiv 手表用于记录 ACC，以及 Epitel Epilog 用于记录 EEG 数据。PPG 和 EDA 信号被记录了多天，然后高质量、边缘质量或低质量数据的时代由评审员视觉识别，并将评审员注释与自动信号质量测量进行比较。对于 ACC，使用 0.8 到 5 Hz 的频谱功率与宽带功率的比率将高质量信号与噪声分开。对于 EDA，振幅变化率和尖峰出现率显着区分了高质量数据和噪声。光谱熵用于评估 PPG，并显示出优质、边缘和劣质信号之间的显着差异。使用识别频谱噪声截止频率的方法评估 EEG 数据。要求患者在几个类别中对每个设备的可用性和舒适性进行评分。患者表现出对腕戴式设备的显着偏好，最常首选的是 Empatica E4 设备。当前的可穿戴设备可以提供高质量的数据并且可以用于常规使用，但需要继续开发以提高数据质量、一致性和管理以及患者的可接受性。