IntroductionAutomated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment.MethodsIn this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0–80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic–clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects).ResultsAt optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic–clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h.DiscussionThese results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.

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使用人工智能自动分析和检测视频记录中的癫痫发作

简介自动癫痫发作检测有望通过协助癫痫诊断和治疗调整来帮助预防 SUDEP 并提高护理质量。方法在这项 2 期探索性研究中，研究了非接触式、无标记、基于视频的运动癫痫检测系统的性能考虑患者（年龄 0-80 岁）的视频记录，根据敏感性、特异性和接受者操作特征 (ROC) 曲线进行评估，并以视频脑电图监测 (VEM) 作为医学金标准。在不同的检测阈值下独立评估五类运动性癫痫发作（强直阵挛、多动、强直、未分类运动、自动症）和运动行为成分持续>10秒的心因性非癫痫发作（PNES）的检测性能（而不是作为分类问题）。该研究总共招募了 230 名患者，其中 81 名患者报告的 VEM 识别出 334 例范围内（>10 秒）运动性癫痫发作（总共 1,114 例癫痫发作）。我们分析了白天和夜间的录音。在一系列值下评估控制阈值以比较灵敏度（n= 81 名癫痫患者）和误检率 (FDR)（n= 所有 230 名受试者）。结果在最佳阈值下，癫痫发作组在敏感性 (CI) 和 FDR/h (CI) 方面的表现：强直 - 阵挛 - 95.2%（82.4，100%）；0.09 (0.077, 0.103)，多动- 92.9% (68.5, 98.7%)；0.64 (0.59, 0.69)，补药- 78.3% (64.4, 87.7%)；5.87 (5.51, 6.23)，自动性- 86.7% (73.5, 97.7%)；3.34 (3.12, 3.58)，未分类运动性癫痫发作 - 78% (65.4, 90.4%)；4.81（4.50、5.14）和 PNES-97.7%（97.7、100%）；1.73（1.61，1.86）。针对所研究的所有运动性癫痫发作推荐的通用阈值声称灵敏度为 88%，FDR/h 为 6.48。讨论这些结果表明主要运动性癫痫发作检测的可实现性能，在临床上可用作诊断工作流程中的癫痫发作筛查解决方案。