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使用耳后的双通道脑电图可穿戴设备准确检测成人和儿童的典型失神发作。

Accurate detection of typical absence seizures in adults and children using a two-channel electroencephalographic wearable behind the ears.

机构信息

Laboratory for Epilepsy Research, KU Leuven and Department of Neurology, University Hospitals, Leuven, Belgium.

Department of Electrical Engineering, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.

出版信息

Epilepsia. 2021 Nov;62(11):2741-2752. doi: 10.1111/epi.17061. Epub 2021 Sep 7.

Abstract

OBJECTIVE

Patients with absence epilepsy sensitivity <10% of their absences. The clinical gold standard to assess absence epilepsy is a 24-h electroencephalographic (EEG) recording, which is expensive, obtrusive, and time-consuming to review. We aimed to (1) investigate the performance of an unobtrusive, two-channel behind-the-ear EEG-based wearable, the Sensor Dot (SD), to detect typical absences in adults and children; and (2) develop a sensitive patient-specific absence seizure detection algorithm to reduce the review time of the recordings.

METHODS

We recruited 12 patients (median age = 21 years, range = 8-50; seven female) who were admitted to the epilepsy monitoring units of University Hospitals Leuven for a 24-h 25-channel video-EEG recording to assess their refractory typical absences. Four additional behind-the-ear electrodes were attached for concomitant recording with the SD. Typical absences were defined as 3-Hz spike-and-wave discharges on EEG, lasting 3 s or longer. Seizures on SD were blindly annotated on the full recording and on the algorithm-labeled file and consequently compared to 25-channel EEG annotations. Patients or caregivers were asked to keep a seizure diary. Performance of the SD and seizure diary were measured using the F1 score.

RESULTS

We concomitantly recorded 284 absences on video-EEG and SD. Our absence detection algorithm had a sensitivity of .983 and false positives per hour rate of .9138. Blind reading of full SD data resulted in sensitivity of .81, precision of .89, and F1 score of .73, whereas review of the algorithm-labeled files resulted in scores of .83, .89, and .87, respectively. Patient self-reporting gave sensitivity of .08, precision of 1.00, and F1 score of .15.

SIGNIFICANCE

Using the wearable SD, epileptologists were able to reliably detect typical absence seizures. Our automated absence detection algorithm reduced the review time of a 24-h recording from 1-2 h to around 5-10 min.

摘要

目的

患有失神癫痫敏感性低于 10%的患者。评估失神癫痫的临床金标准是 24 小时脑电图(EEG)记录,这种记录既昂贵、又具有侵入性且耗时。我们旨在:(1)研究一种非侵入性的、双通道、耳后的基于传感器点(SD)的可穿戴设备,以检测成人和儿童的典型失神发作;(2)开发一种敏感的患者特异性失神发作检测算法,以减少记录的审查时间。

方法

我们招募了 12 名患者(中位年龄=21 岁,范围=8-50;7 名女性),他们被收入鲁汶大学医院的癫痫监测病房,进行 24 小时 25 通道视频-EEG 记录,以评估他们的难治性典型失神发作。另外附加四个耳后的电极与 SD 同时记录。典型失神发作定义为脑电图上的 3-Hz 棘慢波放电,持续 3 秒或更长时间。SD 上的发作由盲法在完整记录和算法标记文件上进行注释,然后与 25 通道 EEG 注释进行比较。患者或护理人员被要求记录发作日记。使用 F1 评分来衡量 SD 和发作日记的性能。

结果

我们在视频-EEG 和 SD 上同时记录了 284 次失神发作。我们的失神发作检测算法的敏感性为.983,每小时的假阳性率为.9138。盲法阅读完整的 SD 数据的敏感性为.81,精度为.89,F1 评分为.73,而审查算法标记的文件则分别得到.83、.89 和.87 的评分。患者自我报告的敏感性为.08,精度为 1.00,F1 评分为.15。

意义

使用可穿戴的 SD,癫痫专家能够可靠地检测到典型的失神发作。我们的自动失神发作检测算法将 24 小时记录的审查时间从 1-2 小时减少到 5-10 分钟左右。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3cb/9292701/b156da54e3d5/EPI-62-2741-g003.jpg

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