Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
Seer Medical, Melbourne, Victoria, Australia.
Epilepsia. 2020 Nov;61 Suppl 1:S25-S35. doi: 10.1111/epi.16527. Epub 2020 Jun 4.
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.
非侵入式可穿戴设备在帮助管理癫痫方面具有巨大潜力，但这些设备必须具有稳健的信号质量，并且患者必须愿意长时间佩戴。可穿戴生物传感器信号的自动化机器学习分类需要对信号质量进行定量测量，以便自动拒绝质量差或损坏的数据段。在这项研究中，将市售的可穿戴传感器放置在接受住院或居家脑电图 (EEG) 监测的癫痫患者和健康志愿者身上。使用 Empatica E4 和 Biovotion Everion 记录加速度计 (ACC)、光体积描记法 (PPG) 和皮肤电活动 (EDA)。使用 Byteflies Sensor Dots 记录 ACC 和 PPG，使用 Activinsights GENEActiv 手表记录 ACC，使用 Epitel Epilog 记录 EEG 数据。PPG 和 EDA 信号记录多日，然后由审阅者目视识别高质量、低质量或低质量数据的时段，并将审阅者注释与自动化信号质量测量进行比较。对于 ACC，使用从 0.8 到 5 Hz 的频谱功率与宽带功率的比值来区分高质量信号和噪声。对于 EDA，幅度变化率和尖锐峰值的出现显著区分了高质量数据和噪声。使用频谱熵评估 PPG，并显示了高质量、低质量和低质量信号之间的显著差异。使用识别光谱噪声截止频率的方法评估 EEG 数据。患者被要求在几个类别中对每个设备的可用性和舒适度进行评分。患者对腕戴设备表现出明显的偏好，而 Empatica E4 设备最受欢迎。目前的可穿戴设备可以提供高质量的数据，并且可以接受常规使用，但需要进一步开发以提高数据质量、一致性和管理水平，以及提高患者的接受程度。
