Department of Neurology, University of Wisconsin-Madison, 1685 Highland Ave, Madison, WI, 53705, USA.
Department of Computer Science, University of Wisconsin-Madison, Madison, USA.
Sci Rep. 2022 Mar 30;12(1):5397. doi: 10.1038/s41598-022-09429-w.
In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogenicity index (EI), phase locked high gamma (PLHG), time and frequency domain Cho Gaines distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, low gamma, and high gamma bands). The deep learning models were pretrained for seizure identification on the time and frequency domains of 1 s, single-channel clips of 127 seizures (from 25 different subjects) using "leave-one-out" (LOO) cross validation. Each neural network extracted unique feature spaces that were interpreted using spectral power modulations before being used to train a Random Forest Classifier (RFC) for seizure identification. The Gini Importance of each feature was calculated from the pretrained RFC, enabling the most significant features (MSFs) for each task to be identified. The MSFs were extracted to train another RFC for UPenn and Mayo Clinic's Seizure Detection Kaggle Challenge. They obtained an AUC score of 0.93, demonstrating a transferable method to identify and interpret biomarkers for seizure detection.
在这项研究中，我们设计了两个深度神经网络，对颅内 EEG 中的早期癫痫发作进行 16 种特征编码，并将它们及其频率响应与 16 种广泛使用的工程指标进行比较，以解释它们的特性：致痫性指数（EI）、锁相高伽马（PLHG）、时间和频域 Cho Gaines 距离（TDCG、FDCG）、相对频带功率和对数绝对频带功率（来自 alpha、beta、theta、delta、低伽马和高伽马频段）。深度学习模型在 1 秒、127 次癫痫发作（来自 25 个不同受试者）的单通道剪辑的时间和频域上进行了癫痫识别的预训练，使用“留一法”（LOO）交叉验证。每个神经网络提取了独特的特征空间，这些特征空间在被用于训练随机森林分类器（RFC）进行癫痫识别之前，使用频谱功率调制进行解释。从预训练的 RFC 中计算了每个特征的基尼重要性，从而可以识别出每个任务的最重要特征（MSFs）。提取 MSFs 以训练另一个用于宾夕法尼亚大学和梅奥诊所癫痫检测 Kaggle 挑战赛的 RFC。他们获得了 0.93 的 AUC 评分，证明了一种可用于识别和解释癫痫检测生物标志物的可转移方法。
