Video-based automatic seizure detection in pharmacoresistant epilepsy: A prospective exploratory study

IF 2.3 3区 医学 Q2 BEHAVIORAL SCIENCES Epilepsy & Behavior Pub Date : 2024-11-13 DOI:10.1016/j.yebeh.2024.110118
Fredrik K. Andersson , Helena Gauffin , Hans Lindehammar , Patrick Vigren
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Abstract

Objective

The objective of this study was to evaluate the diagnostic yield and clinical utility of an automated AI video-based seizure detection device, Nelli®, (SDD) in pharmacoresistant epilepsy patients. The SDD captures and automatically classifies nocturnal motor behavior suggestive of epileptic seizures or non-epileptic motor behavior of potential clinical value.

Methods

Patients with focal epilepsy and pharmacoresistance referred for inpatient long-term video-EEG monitoring were prospectively recruited. Participants were monitored in their home at night with the SDD for a median of 15.5 nights. Captured video recordings were analyzed by clinical experts and each SDD-registration session was classified as diagnostic or not. Clinical utility for each participant was assessed from pre-specified utility measures. The outcome measures were compared between major focal motor and subtle focal motor seizures.

Results

One SDD-registration session in each of the 20 participants was performed and analyzed. Video recordings were captured in 18 sessions. Diagnostic yield was found in 11 registration sessions (55.0 %) and clinical utility in 8 registration sessions (40.0 %). No significant difference was found between the AI-algorithm classification and clinical experts’ consensus assessment of captured video recordings as epileptic or not. Positive predictive value was 81.8 % for registration sessions containing video recordings classified as epileptic seizures. The diagnostic yield and clinical utility were significantly higher among major focal motor seizures (81.8 % and 63.6 %) compared to subtle focal motor seizures.

Significance

The SDD is useful to evaluate patients with pharmacoresistant epilepsy and major focal motor seizures (hyperkinetic, tonic, clonic, focal to bilateral tonic-clonic seizures); it may facilitate the diagnostic process in patients referred for long-term inpatient video-EEG evaluation and beneficially change anti-seizure treatments. The SDD provided accurate classification of major focal motor seizures as epileptic, or non-epileptic, and may serve as a useful diagnostic tool to distinguish epileptic and non-epileptic episodic events with a prominent motor component.
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基于视频的抗药性癫痫发作自动检测:前瞻性探索研究
研究目的本研究旨在评估基于人工智能视频的自动癫痫发作检测设备 Nelli® (SDD) 在药物耐药性癫痫患者中的诊断率和临床实用性。SDD 可捕捉并自动分类提示癫痫发作的夜间运动行为或具有潜在临床价值的非癫痫运动行为:方法:前瞻性招募被转诊至住院部接受长期视频脑电图监测的局灶性癫痫和药物抵抗患者。参与者夜间在家中接受 SDD 监测,监测时间中位数为 15.5 晚。采集的视频记录由临床专家进行分析,每次 SDD 注册会话都会被分为诊断与否。每位受试者的临床效用均根据预先指定的效用指标进行评估。结果比较了大局灶运动性癫痫发作和细微局灶运动性癫痫发作:对 20 名参与者每人进行了一次 SDD 登记并进行了分析。在 18 个疗程中采集了视频记录。在 11 次登记中发现了诊断率(55.0%),在 8 次登记中发现了临床实用性(40.0%)。人工智能算法分类与临床专家对捕获的视频记录是否为癫痫的共识评估之间没有发现明显差异。在包含被归类为癫痫发作的视频记录的登记会话中,阳性预测值为 81.8%。与细微的局灶性运动性癫痫发作相比,大局灶性运动性癫痫发作的诊断率和临床实用性明显更高(分别为 81.8% 和 63.6%):SDD可用于评估具有药物耐药性的癫痫患者和主要局灶性运动发作(过度运动、强直、阵挛性发作、局灶性至双侧强直阵挛性发作);它可促进转诊患者进行长期住院视频脑电图评估的诊断过程,并有益于改变抗癫痫治疗方法。SDD 可将主要的局灶性运动性发作准确地分类为癫痫性或非癫痫性发作,并可作为一种有用的诊断工具,用于区分具有显著运动成分的癫痫性和非癫痫性发作事件。
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来源期刊
Epilepsy & Behavior
Epilepsy & Behavior 医学-行为科学
CiteScore
5.40
自引率
15.40%
发文量
385
审稿时长
43 days
期刊介绍: Epilepsy & Behavior is the fastest-growing international journal uniquely devoted to the rapid dissemination of the most current information available on the behavioral aspects of seizures and epilepsy. Epilepsy & Behavior presents original peer-reviewed articles based on laboratory and clinical research. Topics are drawn from a variety of fields, including clinical neurology, neurosurgery, neuropsychiatry, neuropsychology, neurophysiology, neuropharmacology, and neuroimaging. From September 2012 Epilepsy & Behavior stopped accepting Case Reports for publication in the journal. From this date authors who submit to Epilepsy & Behavior will be offered a transfer or asked to resubmit their Case Reports to its new sister journal, Epilepsy & Behavior Case Reports.
期刊最新文献
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