Over the last few decades, electroencephalogram (EEG) has become one of the most vital tools used by physicians to diagnose several neurological disorders of the human brain and, in particular, to detect seizures. Because of its peculiar nature, the consequent impact of epileptic seizures on the quality of life of patients made the precise diagnosis of epilepsy extremely essential. Therefore, this paper proposes a novel deep-learning approach for detecting seizures in pediatric patients based on the classification of raw multichannel EEG signal recordings that are minimally pre-processed. The new approach takes advantage of the automatic feature learning capabilities of a two-dimensional deep convolutional autoencoder linked to a neural network-based classifier to form a unified system that is trained in a supervised way to achieve the best classification accuracy between the ictal and interictal brain state signals. For testing and evaluating our approach, two models were designed and assessed using three different EEG data segment lengths and a 10-fold cross-validation scheme. Based on five evaluation metrics, the best performing model was a supervised deep convolutional deep autoencoder (SDCAE) model that uses a bidirectional long short-term memory (Bi-LSTM) – based classifier, and EEG segment length of four seconds. Using the public dataset collected from the Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), this model has obtained 98.79 ± 0.53% accuracy, 98.72 ± 0.77% sensitivity, 98.86 ± 0.53% specificity, 98.86 ± 0.53% precision, and an F1-score of 98.79 ± 0.53% respectively. Based on these results, our new approach was able to present one of the most effective seizure detection methods compared to other existing state-of-the-art methods applied to the same dataset.

中文翻译：

---

一种用于癫痫患儿自动癫痫发作检测的深度学习方法

在过去的几十年中，脑电图（EEG）已成为医生用来诊断人脑的几种神经系统疾病，尤其是检测癫痫发作的最重要工具之一。由于其独特的性质，因此癫痫发作对患者生活质量的影响使得对癫痫的精确诊断极为重要。因此，本文基于对经过最少预处理的原始多通道EEG信号记录的分类，提出了一种新颖的深度学习方法，用于检测小儿患者的癫痫发作。新方法利用了二维深度卷积自动编码器的自动特征学习功能，该功能与基于神经网络的分类器链接在一起，形成了一个统一的系统，该系统以受监督的方式进行训练，从而实现了井壁和井壁之间的最佳分类精度。脑状态信号。为了测试和评估我们的方法，使用三种不同的EEG数据段长度和10倍交叉验证方案设计和评估了两个模型。基于五个评估指标，性能最佳的模型是有监督的深度卷积深度自动编码器（SDCAE）模型，该模型使用基于双向长短期记忆（Bi-LSTM）的分类器，并且EEG段长度为4秒。使用从波士顿儿童医院（CHB）和麻省理工学院（MIT）收集的公共数据集，该模型获得了98.79±0.53％的准确性，98.72±0.77％的敏感性，98.86±0.53％的特异性，98.86±0.53％的准确性，F1分数分别为98.79±0.53％。基于这些结果，与应用于同一数据集的其他现有的最新技术方法相比，我们的新方法能够提供一种最有效的癫痫发作检测方法。