Rommens Nicole, Geertsema Evelien, Jansen Holleboom Lisanne, Cox Fieke, Visser Gerhard
Stichting Epilepsie Instellingen Nederland (SEIN), Postbus 540, Hoofddorp 2130 AM, The Netherlands; Technical Medicine, University of Twente, Postbus 217, Enschede 7500 AE, The Netherlands.
Stichting Epilepsie Instellingen Nederland (SEIN), Postbus 540, Hoofddorp 2130 AM, The Netherlands.
Epilepsy Behav. 2018 Jul;84:99-104. doi: 10.1016/j.yebeh.2018.04.026. Epub 2018 May 11.
User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time.
EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU.
EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h.
EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures.
癫痫监测单元（EMU）的用户安全和诊断质量取决于对癫痫发作的反应。在线癫痫发作检测可能会改善这一点。虽然报告了良好的敏感性和特异性，但相对于工作人员的反应而言，其附加价值尚不清楚。我们确定了两种脑电图（EEG）癫痫发作检测算法在额外检测到的癫痫发作或更快检测时间方面的附加价值。
纳入了在一年内入住EMU的患者的EEG-视频癫痫发作记录，每位受试者最多记录两次癫痫发作。使用Encevis EpiScan和BESA Epilepsy对所有记录进行回顾性分析。将算法的检测敏感性和潜伏期与工作人员的反应进行比较。在对连续入住EMU的受试者进行的30次不间断记录（每位受试者约24小时）上估计假阳性率。
使用的EEG-视频记录包括188次癫痫发作。工作人员的反应率为67%，Encevis为67%，BESA Epilepsy为65%。在工作人员漏诊的62次癫痫发作中，Encevis识别出66%，BESA Epilepsy识别出39%。中位潜伏期分别为31秒（工作人员）、10秒（Encevis）和14秒（BESA Epilepsy）。在纠正从观察室到受试者的步行时间后，两种算法在65%的检测到的癫痫发作中比工作人员检测得更快。完整记录包括617小时的EEG。Encevis的中位假阳性率为每24小时4.9次，BESA Epilepsy为每24小时2.1次。
EEG-视频癫痫发作检测算法可通过增加检测到的癫痫发作总数和提高检测速度来改善对癫痫发作的反应。假阳性率在临床情况下是可行的。实施这些算法可能会导致更快的诊断测试和癫痫发作期间更好的观察。
