From the Academic Center for Epileptology (J.A., C.U., P.C., J.v.D., R.L.); Center for Residential Epilepsy Care (F.T.), Kempenhaeghe, Heeze; Faculty of Electrical Engineering (J.A., C.U., P.C., J.V.D., R.L.), Eindhoven University of Technology; Leiden University Medical Centre (R.D.T.); SEIN-Stichting Epilepsie Instellingen Nederland, Heemstede and Zwolle (R.D.T., T.G., A.d.W., B.V., W.H.); and Brain Center Rudolf Magnus (J.v.A., F.L.), Department of Neurology, and Julius Center for Health Sciences and Primary Care (G.v.T., K.C.B.R.), University Medical Center Utrecht, the Netherlands.
Neurology. 2018 Nov 20;91(21):e2010-e2019. doi: 10.1212/WNL.0000000000006545. Epub 2018 Oct 24.
To develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement.
In this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with >1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months. Major seizures were tonic-clonic, generalized tonic >30 seconds, hyperkinetic, or others, including clusters (>30 minutes) of short myoclonic/tonic seizures. The video of all events (alarms, nurse diaries) and 10% completely screened nights were reviewed to classify major (needing an alarm), minor (needing no alarm), or no seizure. Reliability was tested by interobserver agreement. We determined device performance, compared it to a bed sensor (Emfit), and evaluated the caregivers' user experience.
Twenty-eight of 34 admitted participants (1,826 nights, 809 major seizures) completed the study. Interobserver agreement (major/no major seizures) was 0.77 (95% confidence interval [CI] 0.65-0.89). Median sensitivity per participant amounted to 86% (95% CI 77%-93%); the false-negative alarm rate was 0.03 per night (95% CI 0.01-0.05); and the positive predictive value was 49% (95% CI 33%-64%). The multimodal sensor showed a better sensitivity than the bed sensor (n = 14, median difference 58%, 95% CI 39%-80%, < 0.001). The caregivers' questionnaire (n = 33) indicated good sensor acceptance and usability according to 28 and 27 participants, respectively.
Combining heart rate and movement resulted in reliable detection of a broad range of nocturnal seizures.
开发并前瞻性评估一种结合心率和运动的癫痫发作检测方法。
在这项多中心、家庭内、前瞻性、视频对照队列研究中,通过心率(光体积描记法)或运动(3D 加速度计)检测癫痫伴智力障碍患者的夜间发作。每月有>1 次主要发作的参与者在夜间将臂带(Nightwatch)佩戴在上臂 2-3 个月。主要发作包括强直-阵挛、全身性强直>30 秒、多动性或其他发作,包括(>30 分钟)短肌阵挛/强直发作簇。所有事件(报警、护士日记)的视频和 10%完全筛查的夜间被回顾以分类主要(需要报警)、次要(不需要报警)或无发作。通过观察者间一致性测试可靠性。我们确定了设备性能,将其与床传感器(Emfit)进行了比较,并评估了护理人员的用户体验。
34 名入组参与者中的 28 名(1826 个夜晚,809 次主要发作)完成了研究。观察者间一致性(主要/非主要发作)为 0.77(95%置信区间 [CI] 0.65-0.89)。每位参与者的中位敏感性为 86%(95% CI 77%-93%);假阴性报警率为每晚 0.03(95% CI 0.01-0.05);阳性预测值为 49%(95% CI 33%-64%)。多模态传感器的敏感性优于床传感器(n=14,中位差异 58%,95% CI 39%-80%,<0.001)。根据 28 名和 27 名参与者的情况,护理人员调查问卷(n=33)表明传感器具有良好的接受度和可用性。
心率和运动的结合可可靠地检测到广泛的夜间发作。