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癫痫发作检测设备

Seizure Detection Devices.

作者信息

Baumgartner Christoph, Baumgartner Jakob, Lang Clemens, Lisy Tamara, Koren Johannes P

机构信息

Department of Neurology, Clinic Hietzing, 1130 Vienna, Austria.

Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, 1130 Vienna, Austria.

出版信息

J Clin Med. 2025 Jan 28;14(3):863. doi: 10.3390/jcm14030863.

DOI:10.3390/jcm14030863
PMID:39941534
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11818620/
Abstract

Goals of automated detection of epileptic seizures using wearable devices include objective documentation of seizures, prevention of sudden unexpected death in epilepsy (SUDEP) and seizure-related injuries, obviating both the unpredictability of seizures and potential social embarrassment, and finally to develop seizure-triggered on-demand therapies. Automated seizure detection devices are based on the analysis of EEG signals (scalp-EEG, subcutaneous EEG and intracranial EEG), of motor manifestations of seizures (surface EMG, accelerometry), and of physiologic autonomic changes caused by seizures (heart and respiration rate, oxygen saturation, sweat secretion, body temperature). While the detection of generalized tonic-clonic and of focal to bilateral tonic-clonic seizures can be achieved with high sensitivity and low false alarm rates, the detection of focal seizures is still suboptimal, especially in the everyday ambulatory setting. Multimodal seizure detection devices in general provide better performance than devices based on single measurement parameters. Long-term use of seizure detection devices in home environments helps to improve the accuracy of seizure diaries and to reduce seizure-related injuries, while evidence for prevention of SUDEP is still lacking. Automated seizure detection devices are generally well accepted by patients and caregivers.

摘要

使用可穿戴设备自动检测癫痫发作的目标包括癫痫发作的客观记录、预防癫痫猝死(SUDEP)和与发作相关的损伤、消除发作的不可预测性和潜在的社交尴尬,以及最终开发由发作触发的按需治疗。自动发作检测设备基于对脑电图信号(头皮脑电图、皮下脑电图和颅内脑电图)、发作的运动表现(表面肌电图、加速度测量)以及由发作引起的生理自主变化(心率和呼吸率、血氧饱和度、汗液分泌、体温)的分析。虽然全身性强直阵挛发作和局灶性至双侧强直阵挛发作的检测可以实现高灵敏度和低误报率,但局灶性发作的检测仍然不理想,尤其是在日常门诊环境中。一般来说,多模态发作检测设备比基于单一测量参数的设备性能更好。在家庭环境中长期使用发作检测设备有助于提高发作日记的准确性并减少与发作相关的损伤,而预防SUDEP的证据仍然不足。自动发作检测设备通常受到患者和护理人员的广泛接受。

相似文献

1
Seizure Detection Devices.癫痫发作检测设备
J Clin Med. 2025 Jan 28;14(3):863. doi: 10.3390/jcm14030863.
2
Non-electroencephalogram-based seizure detection devices: State of the art and future perspectives.基于非脑电图的癫痫发作检测设备:现状与未来展望。
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Automatic Computer-Based Detection of Epileptic Seizures.基于计算机的癫痫发作自动检测
Front Neurol. 2018 Aug 9;9:639. doi: 10.3389/fneur.2018.00639. eCollection 2018.
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Are Seizure Detection Devices Ready for Prime Time?癫痫检测设备准备好投入实际应用了吗?
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Strategies for non-EEG seizure detection and timing for alerting and interventions with tonic-clonic seizures.强直-阵挛性发作的非 EEG 发作检测和预警及干预时机策略。
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Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic.癫痫发作日记与可穿戴设备预测:门诊外的癫痫监测
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Non-EEG based ambulatory seizure detection designed for home use: What is available and how will it influence epilepsy care?为家庭使用设计的基于非脑电图的动态癫痫发作检测:有哪些可用的产品,以及它将如何影响癫痫护理?
Epilepsy Behav. 2016 Apr;57(Pt A):82-89. doi: 10.1016/j.yebeh.2016.01.003. Epub 2016 Feb 27.

本文引用的文献

1
Subcutaneous electroencephalography monitoring for people with epilepsy and intellectual disability: co-production workshops.癫痫和智力残疾患者的皮下脑电图监测:联合制作工作坊
BJPsych Open. 2024 Dec 13;11(1):e3. doi: 10.1192/bjo.2024.825.
2
Sudden death in epilepsy: the overlap between cardiac and neurological factors.癫痫猝死:心脏与神经因素的重叠
Brain Commun. 2024 Oct 1;6(5):fcae309. doi: 10.1093/braincomms/fcae309. eCollection 2024.
3
Clinical utility of ultra long-term subcutaneous electroencephalographic monitoring in drug-resistant epilepsies: a "real world" pilot study.超长期皮下脑电图监测在耐药性癫痫中的临床应用:一项“真实世界”的初步研究。
Epilepsia. 2024 Nov;65(11):3265-3278. doi: 10.1111/epi.18121. Epub 2024 Sep 28.
4
The spectrum of indications for ultralong-term EEG monitoring.超长程脑电图监测的适应证谱。
Seizure. 2024 Oct;121:262-270. doi: 10.1016/j.seizure.2024.08.015. Epub 2024 Aug 22.
5
Automated detection of tonic seizures using wearable movement sensor and artificial neural network.使用可穿戴运动传感器和人工神经网络自动检测强直发作。
Epilepsia. 2024 Sep;65(9):e170-e174. doi: 10.1111/epi.18077. Epub 2024 Jul 30.
6
Alzheimer's Disease and Epilepsy: Exploring Shared Pathways and Promising Biomarkers for Future Treatments.阿尔茨海默病与癫痫:探索共同通路及未来治疗的潜在生物标志物
J Clin Med. 2024 Jul 1;13(13):3879. doi: 10.3390/jcm13133879.
7
Prospective multicenter study of continuous tonic-clonic seizure monitoring on Apple Watch in epilepsy monitoring units and ambulatory environments.前瞻性多中心研究:在癫痫监测单元和动态环境中使用 Apple Watch 进行持续强直阵挛性癫痫发作监测。
Epilepsy Behav. 2024 Sep;158:109908. doi: 10.1016/j.yebeh.2024.109908. Epub 2024 Jul 3.
8
Seizure-Related Head Injuries: A Narrative Review.癫痫相关头部损伤:一篇叙述性综述
Brain Sci. 2024 May 8;14(5):473. doi: 10.3390/brainsci14050473.
9
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection.多模态可穿戴 EEG、EMG 和加速度计测量可提高强直阵挛性癫痫发作检测的准确性。
Physiol Meas. 2024 Jun 7;45(6). doi: 10.1088/1361-6579/ad4e94.
10
Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.利用市售数字手表可靠检测全面性癫痫发作:多中心 2 期研究。
Epilepsia. 2024 Jul;65(7):2054-2068. doi: 10.1111/epi.17974. Epub 2024 May 13.