IEEE J Biomed Health Inform. 2021 Aug;25(8):2997-3008. doi: 10.1109/JBHI.2021.3049649. Epub 2021 Aug 5.
Timely detection of seizures is crucial to implement optimal interventions, and may help reduce the risk of sudden unexpected death in epilepsy (SUDEP) in patients with generalized tonic-clonic seizures (GTCSs). While video-based automated seizure detection systems may be able to provide seizure alarms in both in-hospital and at-home settings, earlier studies have primarily employed hand-designed features for such a task. In contrast, deep learning-based approaches do not rely on prior feature selection and have demonstrated outstanding performance in many data classification tasks. Despite these advantages, neural network-based video classification has rarely been attempted for seizure detection. We here assessed the feasibility and efficacy of automated GTCSs detection from videos using deep learning. We retrospectively identified 76 GTCS videos from 37 participants who underwent long-term video-EEG monitoring (LTM) along with interictal video data from the same patients, and 10 full-night seizure-free recordings from additional patients. Using a leave-one-subject-out cross-validation approach (LOSO-CV), we evaluated the performance to detect seizures based on individual video frames (convolutional neural networks, CNNs) or video sequences [CNN+long short-term memory (LSTM) networks]. CNN+LSTM networks based on video sequences outperformed GTCS detection based on individual frames yielding a mean sensitivity of 88% and mean specificity of 92% across patients. The average detection latency after presumed clinical seizure onset was 22 seconds. Detection performance increased as a function of training dataset size. Collectively, we demonstrated that automated video-based GTCS detection with deep learning is feasible and efficacious. Deep learning-based methods may be able to overcome some limitations associated with traditional approaches using hand-crafted features, serve as a benchmark for future methods and analyses, and improve further with larger datasets.
及时发现癫痫发作对于实施最佳干预至关重要，并且可能有助于降低全身性强直-阵挛性发作（GTCS）患者突发意外死亡（SUDEP）的风险。虽然基于视频的自动癫痫发作检测系统可能能够在住院和家庭环境中提供发作警报，但早期研究主要采用手工设计的特征来完成此类任务。相比之下，基于深度学习的方法不依赖于先前的特征选择，并且在许多数据分类任务中表现出色。尽管具有这些优势，但基于神经网络的视频分类很少用于癫痫发作检测。我们在此评估了使用深度学习从视频中自动检测 GTCS 的可行性和功效。我们回顾性地从 37 名接受长期视频-脑电图监测（LTM）的参与者中确定了 76 个 GTCS 视频，以及来自同一患者的间歇视频数据，以及另外 10 个来自无癫痫发作的整夜记录。使用留一受试者交叉验证方法（LOSO-CV），我们评估了基于单个视频帧（卷积神经网络，CNN）或视频序列[CNN+长短期记忆（LSTM）网络]检测发作的性能。基于视频序列的 CNN+LSTM 网络在基于单个帧的 GTCS 检测方面表现出色，在患者中产生了 88%的平均敏感性和 92%的平均特异性。在假定的临床发作后，平均检测潜伏期为 22 秒。检测性能随着训练数据集大小的增加而提高。总体而言，我们证明了基于深度学习的自动视频 GTCS 检测是可行且有效的。基于深度学习的方法可能能够克服使用手工制作特征的传统方法所带来的一些限制，为未来的方法和分析提供基准，并通过更大的数据集进一步改进。
