Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands.
Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
Epilepsia. 2018 Jun;59 Suppl 1:53-60. doi: 10.1111/epi.14050. Epub 2018 Apr 11.
People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.
癫痫患者在发生惊厥性癫痫发作（CS）时需要帮助，并且有猝死的风险。自动实时癫痫检测系统可以帮助提醒护理人员，但可穿戴传感器并不总是被接受。我们确定了算法设置，并研究了视频算法在住宅护理环境中检测 CS 的检测性能。该算法计算相对于群速度信号的 2-6 Hz 范围内的功率，群速度信号源自视频序列光流。使用包含 72 个 CS 的视频-脑电图（EEG）记录的训练集找到检测阈值。使用包含 12 名新入住者的 24 个完整夜晚的测试集和 50 个随机选择的 CS 的额外记录来估计性能。所有数据均进行回顾性分析。CS（全身性强直-阵挛性发作和强直-阵挛性发作）的开始和结束以及其他需要检测的发作（全身性强直、运动过度和其他主要发作）均进行了注释。检测阈值设定为在训练集中获得 97%灵敏度的值。在测试集中计算了灵敏度、潜伏期和每夜的假阳性率（FDR）。当算法输出连续超过阈值 2 秒时，即检测到发作。使用在训练集中确定的检测阈值，所有 CS 在测试集中均被检测到（灵敏度为 100%）。78%的检测中潜伏期≤10 秒。检测到 3/5 例运动过度发作和 6/9 例其他主要发作。每夜的中位数 FDR 为 0.78，24 个夜晚中有 9 个没有假阳性。我们的算法可以通过自动实时检测视频记录中的 CS，以可接受的潜伏期和 FDR，不引人注目的地提高安全性。该算法还可以检测需要帮助的其他一些运动性癫痫发作。
