van Andel Judith, Thijs Roland D, de Weerd Al, Arends Johan, Leijten Frans
University Medical Centre Utrecht, Department of Clinical Neurophysiology, Utrecht, The Netherlands.
Stichting Epilepsie Instellingen Nederland SEIN, Department of Clinical Neurophysiology, Heemstede, The Netherlands; Leiden University Medical Centre, Department of Neurology, Leiden, The Netherlands.
Epilepsy Behav. 2016 Apr;57(Pt A):82-89. doi: 10.1016/j.yebeh.2016.01.003. Epub 2016 Feb 27.
This study aimed to (1) evaluate available systems and algorithms for ambulatory automatic seizure detection and (2) discuss benefits and disadvantages of seizure detection in epilepsy care.
PubMed and EMBASE were searched up to November 2014, using variations and synonyms of search terms "seizure prediction" OR "seizure detection" OR "seizures" AND "alarm".
Seventeen studies evaluated performance of devices and algorithms to detect seizures in a clinical setting. Algorithms detecting generalized tonic-clonic seizures (GTCSs) had varying sensitivities (11% to 100%) and false alarm rates (0.2-4/24 h). For other seizure types, detection rates were low, or devices produced many false alarms. Five studies externally validated the performance of four different devices for the detection of GTCSs. Two devices were promising in both children and adults: a mattress-based nocturnal seizure detector (sensitivity: 84.6% and 100%; false alarm rate: not reported) and a wrist-based detector (sensitivity: 89.7%; false alarm rate: 0.2/24 h).
Detection of seizure types other than GTCSs is currently unreliable. Two detection devices for GTCSs provided promising results when externally validated in a clinical setting. However, these devices need to be evaluated in the home setting in order to establish their true value. Automatic seizure detection may help prevent sudden unexpected death in epilepsy or status epilepticus, provided the alarm is followed by an effective intervention. Accurate seizure detection may improve the quality of life (QoL) of subjects and caregivers by decreasing burden of seizure monitoring and may facilitate diagnostic monitoring in the home setting. Possible risks are occurrence of alarm fatigue and invasion of privacy. Moreover, an unexpectedly high seizure frequency might be detected for which there are no treatment options. We propose that future studies monitor benefits and disadvantages of seizure detection systems with particular emphasis on QoL, comfort, and privacy of subjects and impact of false alarms.
本研究旨在(1)评估用于动态自动癫痫发作检测的现有系统和算法,以及(2)讨论癫痫护理中癫痫发作检测的利弊。
截至2014年11月,检索了PubMed和EMBASE,使用检索词“癫痫发作预测”或“癫痫发作检测”或“癫痫发作”以及“警报”的变体和同义词。
17项研究评估了在临床环境中检测癫痫发作的设备和算法的性能。检测全身性强直阵挛性发作(GTCS)的算法具有不同的灵敏度(11%至100%)和误报率(0.2 - 4次/24小时)。对于其他癫痫发作类型,检测率较低,或者设备产生许多误报。5项研究对4种不同设备检测GTCS的性能进行了外部验证。两种设备在儿童和成人中都很有前景:一种基于床垫的夜间癫痫发作检测器(灵敏度:84.6%和100%;误报率:未报告)和一种基于手腕的检测器(灵敏度:89.7%;误报率:0.2次/24小时)。
目前,检测除GTCS之外的癫痫发作类型并不可靠。两种用于GTCS的检测设备在临床环境中进行外部验证时提供了有前景的结果。然而,这些设备需要在家庭环境中进行评估,以确定它们的真正价值。自动癫痫发作检测可能有助于预防癫痫猝死或癫痫持续状态,前提是警报后有有效的干预措施。准确的癫痫发作检测可以通过减轻癫痫发作监测负担来提高受试者和护理人员的生活质量(QoL),并可能有助于家庭环境中的诊断监测。可能的风险是出现警报疲劳和侵犯隐私。此外,可能会检测到意外高的癫痫发作频率,而对此没有治疗选择。我们建议未来的研究监测癫痫发作检测系统的利弊,特别强调受试者的QoL、舒适度和隐私以及误报的影响。