Johns Hopkins University, Department of Neurology, United States.
University of Maryland Medical Center, United States.
Epilepsy Behav. 2024 Sep;158:109908. doi: 10.1016/j.yebeh.2024.109908. Epub 2024 Jul 3.
Evaluate the performance of a custom application developed for tonic-clonic seizure (TCS) monitoring on a consumer-wearable (Apple Watch) device.
Participants with a history of convulsive epileptic seizures were recruited for either Epilepsy Monitoring Unit (EMU) or ambulatory (AMB) monitoring; participants without epilepsy (normal controls [NC]) were also enrolled in the AMB group. Both EMU and AMB participants wore an Apple Watch with a research app that continuously recorded accelerometer and photoplethysmography (PPG) signals, and ran a fixed-and-frozen tonic-clonic seizure detection algorithm during the testing period. This algorithm had been previously developed and validated using a separate training dataset. All EMU convulsive events were validated by video-electroencephalography (video-EEG); AMB events were validated by caregiver reporting and follow-ups. Device performance was characterized and compared to prior monitoring devices through sensitivity, false alarm rate (FAR; false-alarms per 24 h), precision, and detection delay (latency).
The EMU group had 85 participants (4,279 h, 19 TCS from 15 participants) enrolled across four EMUs; the AMB group had 21 participants (13 outpatient, 8 NC, 6,735 h, 10 TCS from 3 participants). All but one AMB participant completed the study. Device performance in the EMU group included a sensitivity of 100 % [95 % confidence interval (CI) 79-100 %]; an FAR of 0.05 [0.02, 0.08] per 24 h; a precision of 68 % [48 %, 83 %]; and a latency of 32.07 s [standard deviation (std) 10.22 s]. The AMB group had a sensitivity of 100 % [66-100 %]; an FAR of 0.13 [0.08, 0.24] per 24 h; a precision of 22 % [11 %, 37 %]; and a latency of 37.38 s [13.24 s]. Notably, a single AMB participant was responsible for 8 of 31 false alarms. The AMB FAR excluding this participant was 0.10 [0.07, 0.14] per 24 h.
This study demonstrates the practicability of TCS monitoring on a popular consumer wearable (Apple Watch) in daily use for people with epilepsy. The monitoring app had a high sensitivity and a substantially lower FAR than previously reported in both EMU and AMB environments.
评估一款针对强直阵挛性癫痫发作(TCS)监测而开发的定制应用在消费级可穿戴设备(Apple Watch)上的性能。
招募了有癫痫性抽搐病史的参与者,他们分别接受癫痫监测单元(EMU)或门诊(AMB)监测;无癫痫(正常对照[NC])的参与者也被纳入 AMB 组。EMU 和 AMB 组参与者均佩戴配备研究应用的 Apple Watch,该应用可连续记录加速度计和光容积描记图(PPG)信号,并在测试期间运行固定和冻结的强直阵挛性癫痫发作检测算法。该算法之前是使用单独的训练数据集开发和验证的。所有 EMU 的惊厥事件均通过视频脑电图(video-EEG)进行验证;AMB 事件通过护理人员报告和随访进行验证。通过敏感性、误报率(FAR;每 24 小时的误报数)、精确度和检测延迟(潜伏期),对设备性能进行了特征描述并与之前的监测设备进行了比较。
EMU 组有 85 名参与者(4279 小时,15 名参与者中的 19 次 TCS)在四个 EMU 中接受监测;AMB 组有 21 名参与者(13 名门诊患者,8 名 NC,6735 小时,3 名参与者中的 10 次 TCS)。除了一名 AMB 参与者外,所有参与者均完成了研究。EMU 组的设备性能包括敏感性为 100%[95%置信区间(CI)79-100%];误报率为 0.05[0.02,0.08]每 24 小时;精确度为 68%[48%,83%];潜伏期为 32.07 秒[标准差(std)为 10.22 秒]。AMB 组的敏感性为 100%[66-100%];误报率为 0.13[0.08,0.24]每 24 小时;精确度为 22%[11%,37%];潜伏期为 37.38 秒[13.24 秒]。值得注意的是,一名 AMB 参与者的 31 次误报中有 8 次是单独造成的。排除该参与者后,AMB 的误报率为 0.10[0.07,0.14]每 24 小时。
这项研究证明了在日常使用中,TCS 监测在流行的消费级可穿戴设备(Apple Watch)上的实用性。与 EMU 和 AMB 环境中的先前报告相比,该监测应用的敏感性高,误报率显著降低。