Rai Pragya, Knight Andrew, Hiillos Matias, Kertész Csaba, Morales Elizabeth, Terney Daniella, Larsen Sidsel Armand, Østerkjerhuus Tim, Peltola Jukka, Beniczky Sándor
Neuro Event Labs, Tampere, Finland.
Department of Medicine and Health Technology, Tampere University, Tampere, Finland.
Front Neuroinform. 2024 Mar 15;18:1324981. doi: 10.3389/fninf.2024.1324981. eCollection 2024.
Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment.
In 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 ( = 81 subjects with seizures) and false detection rate (FDR) ( = all 230 subjects).
At 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.
These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.
自动癫痫发作检测有望通过辅助癫痫诊断和治疗调整来预防癫痫猝死(SUDEP)并提高护理质量。
在这项2期探索性研究中,评估了一种基于视频的非接触式、无标记运动性癫痫发作检测系统的性能,该系统针对0至80岁患者的视频记录,以视频脑电图监测(VEM)作为医学金标准,从灵敏度、特异性和受试者工作特征(ROC)曲线方面进行评估。独立评估了五类运动性癫痫发作(强直阵挛性、运动增多性、强直性、未分类运动性、自动症)和具有持续超过10秒运动行为成分的精神性非癫痫发作(PNES)在不同检测阈值下的检测性能(而非作为分类问题)。该研究共招募了230名患者,其中81名患者报告的VEM识别出1114次发作中的334次符合条件(>10秒)的运动性癫痫发作。我们分析了白天和夜间的记录。在一系列值范围内评估控制阈值,以比较灵敏度(=81名有癫痫发作的受试者)和误检率(FDR)(=所有230名受试者)。
在最佳阈值下,癫痫发作组在灵敏度(CI)和FDR/h(CI)方面的表现如下:强直阵挛性发作 - 95.2%(82.4,100%);0.09(0.077,0.103),运动增多性发作 - 92.9%(68.5,98.7%);0.64(0.59,0.69),强直性发作 - 78.3%(64.4,87.7%);5.87(5.51,6.23),自动症发作 - 86.7%(73.5,97.7%);3.34(3.12,3.58),未分类运动性癫痫发作 -