Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA.
Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Epilepsia. 2023 Dec;64 Suppl 4(Suppl 4):S114-S123. doi: 10.1111/epi.17265. Epub 2022 May 4.
This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.
本研究描述了一种使用具有超长程皮下 EEG（sqEEG）记录的递归神经网络的广义跨患者发作预测方法。使用来自六名被诊断为难治性癫痫并使用 sqEEG 设备监测的患者的数据，使用长短期记忆（LSTM）深度学习分类器为发作预测开发了一种广义算法。电发作由经过董事会认证的癫痫专家确定。根据与确认的发作的关系，将 1 分钟数据段标记为发作前或发作间。将数据分为训练和测试数据集，为了补偿训练中数据不平衡的比例，生成了添加噪声的发作前数据段副本以扩展训练数据集。使用训练数据的平均值和标准差（SD）对所有数据进行归一化，保留分析的伪前瞻性性质。使用留一患者交叉验证方法训练和测试不同架构的分类器，并使用接收者操作特征（ROC）曲线下面积（AUC）评估分类器的性能。使用每次分类器的重复训练和测试的留一信号法评估每个输入信号的重要性。在六个患者中的四个中，跨患者分类器的性能明显优于随机，总体平均 AUC 为 0.602±0.126（平均值±标准差）。对于结果优于随机的患者，观察到警告时间为 37.386%±5.006%（平均值±标准差）和灵敏度为 0.691±0.068（平均值±标准差）。对输入通道的分析表明，信号通道的傅立叶变换对整体分类器性能有显著贡献（p<0.05）。输入信号的相对贡献在患者和架构之间有所不同，这表明包括所有信号有助于在跨患者分类器中具有稳健性。这些早期结果表明，使用双通道超长程 sqEEG 从不同患者的数据中进行训练可以预测发作。
