The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on the intracranial EEG data to make a surgical decision related to epileptic subjects suffering from uncontrollable seizures. This surgery usually aims to remove the epileptogenic region which requires precise characterization of that area using the EEG recordings. In this paper, we propose two methods based on deep learning targeting accurate automatic epileptic focus localization using the non-stationary EEG recordings. Our first proposed method is based on semi-supervised learning, in which a deep convolutional autoencoder is trained and then the pre-trained encoder is used with multi-layer perceptron as a classifier. The goal is to determine the location of the EEG signal that is responsible for the epileptic activity. In the second proposed method, unsupervised learning scheme is implemented by merging deep convolutional variational autoencoder and K-means algorithm for clustering the iEEG signals into two distinct clusters based on the seizure source. The proposed methods automate and integrate the features extraction and classification processes instead of manually extracting the features as done in the previous studies. Dimensionality reduction is achieved using the autoencoder, while the important spatio-temporal features are extracted from the EEG recordings using the convolutional layers. Moreover, we implemented the inference network of the semi-supervised model on FPGA. The results of our experiments demonstrate high classification accuracy and clustering performance in localizing the epileptic focus compared with the state of the art.

中文翻译：

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癫痫病灶局部化的深度学习方法。

由于其在有效的癫痫手术中的作用，癫痫病灶定位的任务受到了极大的关注。临床医生高度依赖于颅内EEG数据做出与癫痫患者遭受不可控癫痫发作有关的手术决策。该手术通常旨在去除癫痫发生区域，该区域需要使用EEG记录精确表征该区域。在本文中，我们提出了两种基于深度学习的方法，该方法基于使用非平稳EEG记录的准确自动癫痫病灶的局部定位。我们首先提出的方法是基于半监督学习的，其中训练了深度卷积自动编码器，然后将预训练的编码器与多层感知器一起用作分类器。目的是确定负责癫痫活动的EEG信号的位置。在第二种提出的方​​法中，通过结合深度卷积变分自编码器和K-means算法，基于癫痫发作源将iEEG信号分为两个不同的群集，实现了无监督学习方案。所提出的方法使特征提取和分类过程自动化和集成，而不是像以前的研究那样手动提取特征。使用自动编码器可实现降维，同时使用卷积层从EEG记录中提取重要的时空特征。此外，我们在FPGA上实现了半监督模型的推理网络。