Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.
RISE Acreo AB, Gothenburg, Sweden.
Seizure. 2019 Feb;65:48-54. doi: 10.1016/j.seizure.2018.12.024. Epub 2018 Dec 27.
The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate training and test data sets.
Seventy-five epilepsy surgery candidates undergoing video-EEG monitoring were included. The patients wore one three-axis accelerometer on each wrist during video-EEG. The accelerometer data was band-pass filtered and reduced using a movement threshold and mapped to a time-frequency feature space representation. Algorithms based on standard binary classifiers combined with a TCS specific event detection layer were developed and trained using the training set. Their performance was evaluated in terms of sensitivity and false positive (FP) rate using the test set.
Thirty-seven available TCSs in 11 patients were recorded and the data was divided into disjoint training (27 TCSs, three patients) and test (10 TCSs, eight patients) data sets. The classification algorithms evaluated were K-nearest-neighbors (KNN), random forest (RF) and a linear kernel support vector machine (SVM). For the TCSs detection performance of the three algorithms in the test set, the highest sensitivity was obtained for KNN (100% sensitivity, 0.05 FP/h) and the lowest FP rate was obtained for RF (90% sensitivity, 0.01 FP/h).
The low FP rate enhances the clinical utility of the detection system for long-term reliable seizure monitoring. It also allows a possible implementation of an automated TCS detection in free-living environment, which could contribute to ascertain seizure frequency and thereby better seizure management.
本前瞻性、视频脑电图（video-EEG）对照研究旨在评估基于加速度计的可穿戴系统检测强直阵挛性发作（TCSs）的性能，并使用单独的训练和测试数据集研究不同的检测算法的准确性。
本研究纳入了 75 名接受视频脑电图监测的癫痫手术候选者。这些患者在视频脑电图监测期间在每个手腕上佩戴一个三轴加速度计。将加速度计数据进行带通滤波和运动阈值处理，并映射到时频特征空间表示。基于标准二进制分类器的算法与 TCS 特定的事件检测层相结合，使用训练集进行开发和训练。使用测试集评估其性能，以灵敏度和假阳性（FP）率为指标。
记录了 11 名患者中 37 个可获得的 TCS，并将数据分为不相交的训练（27 个 TCS，3 名患者）和测试（10 个 TCS，8 名患者）数据集。评估的分类算法包括 K-最近邻（KNN）、随机森林（RF）和线性核支持向量机（SVM）。对于三种算法在测试集中的 TCS 检测性能，KNN 的灵敏度最高（100%灵敏度，0.05 FP/h），RF 的 FP 率最低（90%灵敏度，0.01 FP/h）。
低 FP 率提高了检测系统用于长期可靠的发作监测的临床实用性。它还允许在自由生活环境中实现自动化 TCS 检测，这有助于确定发作频率，从而更好地管理发作。
