Choi WooHyeok, Kim Min-Jee, Yum Mi-Sun, Jeong Dong-Hwa
School of Computer Science and Information Engineering, The Catholic University of Korea, Seoul 14662, Korea.
Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, Seoul 05505, Korea.
J Pers Med. 2022 May 9;12(5):763. doi: 10.3390/jpm12050763.
The early prediction of epileptic seizures is important to provide appropriate treatment because it can notify clinicians in advance. Various EEG-based machine learning techniques have been used for automatic seizure classification based on subject-specific paradigms. However, because subject-specific models tend to perform poorly on new patient data, a generalized model with a cross-patient paradigm is necessary for building a robust seizure diagnosis system. In this study, we proposed a generalized model that combines one-dimensional convolutional layers (1D CNN), gated recurrent unit (GRU) layers, and attention mechanisms to classify preictal and interictal phases. When we trained this model with ten minutes of preictal data, the average accuracy over eight patients was 82.86%, with 80% sensitivity and 85.5% precision, outperforming other state-of-the-art models. In addition, we proposed a novel application of attention mechanisms for channel selection. The personalized model using three channels with the highest attention score from the generalized model performed better than when using the smallest attention score. Based on these results, we proposed a model for generalized seizure predictors and a seizure-monitoring system with a minimized number of EEG channels.
癫痫发作的早期预测对于提供适当治疗很重要，因为它可以提前通知临床医生。基于脑电图（EEG）的各种机器学习技术已被用于基于特定个体范式的癫痫发作自动分类。然而，由于特定个体模型在新患者数据上的表现往往不佳，因此需要一个具有跨患者范式的通用模型来构建强大的癫痫发作诊断系统。在本研究中，我们提出了一种通用模型，该模型结合了一维卷积层（1D CNN）、门控循环单元（GRU）层和注意力机制，以对发作前期和发作间期进行分类。当我们用十分钟的发作前期数据训练该模型时，八名患者的平均准确率为82.86%，灵敏度为80%，精确率为85.5%，优于其他现有技术模型。此外，我们还提出了一种用于通道选择注意力机制的新应用。使用来自通用模型中注意力得分最高的三个通道的个性化模型比使用注意力得分最低的通道表现更好。基于这些结果，我们提出了一种用于通用癫痫发作预测器的模型以及一个具有最少EEG通道数量的癫痫发作监测系统。
