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使用人工智能摄像头改善社区中智障人士的夜间事件监测。

Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera.

作者信息

Lennard Sarah, Newman Rachel, McLean Brendan, Jory Caryn, Cox David, Young Charlotte, Corson Eve, Shankar Rohit

机构信息

Department of Intellectual Disability Neuropsychiatry, Research Team, Cornwall Partnership NHS Foundation Trust, Truro TR1 3QB, UK.

Cornwall Intellectual Disability Equitable Research (CIDER), University of Plymouth Peninsula School of Medicine, Truro TR1 3FF, UK.

出版信息

Epilepsy Behav Rep. 2023 Apr 23;22:100603. doi: 10.1016/j.ebr.2023.100603. eCollection 2023.

Abstract

There is higher prevalence of epilepsy and SUDEP in people with intellectual disability (PwID) compared to general population. Accurate seizure recording particularly at night can be challenging in PwID. Neuro Event Labs seizure monitoring (Nelli) uses high-quality video based artificial intelligence to detect and record possible nocturnal seizures. This study looks to evaluate the clinical utility and acceptability of Nelli in PwID and epilepsy. Family/carers of PwID and drug resistant epilepsy with suspicions of nocturnal seizures who had not tolerated routine or ambulatory EEGs were invited to evaluate Nelli. Relevant demographics and clinical characteristics were collected. Nelli's impact, it's facilitators, barriers and feedback quality was captured from patient and professional stakeholders. Quantitative and thematic analysis was undertaken. Fifteen PwID and epilepsy and four health professionals were involved. Nelli recorded 707 possible seizure events across the study cohort of which 247 were not heard or recognised by carers. Carers recorded 165 episodes of 'restless' or "seizure behaviour" which the Nelli did not deem to be seizures. There was 93% acceptability. Thematic analysis revealed three broad themes of device acceptability, result implementation and possible seizure recognition ability. Nelli allowed for improved communication and care planning in a hitherto difficult to investigate population.

摘要

与普通人群相比,智力残疾者(PwID)中癫痫和不明原因癫痫猝死(SUDEP)的患病率更高。对PwID来说,准确记录癫痫发作尤其是夜间发作具有挑战性。神经事件实验室癫痫监测(Nelli)使用基于高质量视频的人工智能来检测和记录可能的夜间癫痫发作。本研究旨在评估Nelli在PwID和癫痫患者中的临床实用性和可接受性。邀请了PwID以及患有耐药性癫痫且怀疑有夜间癫痫发作但无法耐受常规或动态脑电图检查的患者的家属/护理人员来评估Nelli。收集了相关的人口统计学和临床特征。从患者和专业利益相关者那里获取了Nelli的影响、促进因素、障碍和反馈质量。进行了定量和主题分析。共有15名PwID和癫痫患者以及4名卫生专业人员参与。在整个研究队列中,Nelli记录了707次可能的癫痫发作事件,其中247次未被护理人员听到或识别。护理人员记录了165次“不安”或“癫痫发作行为”发作,而Nelli认为这些不是癫痫发作。其可接受性为93%。主题分析揭示了设备可接受性、结果实施和可能的癫痫识别能力这三个广泛主题。Nelli有助于在一个迄今为止难以调查的人群中改善沟通和护理计划。

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