Abstract：
One of the epileptic patients\' challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients.
摘要：
癫痫患者的挑战之一是检测癫痫发作的时间和预测的可能性。这项研究旨在提供一种基于深度学习的算法，以检测和预测癫痫发作发生前一到两分钟的时间。所提出的卷积神经网络（CNN）可以通过单导联ECG信号处理而不是使用EEG信号来检测和预测局灶性癫痫发作的发生。所提出的用于癫痫发作检测和预测的CNN的结构是相同的。考虑到可穿戴系统的要求，经过几个轻微的预处理步骤，ECG信号可以用作神经网络的输入，而无需任何手动特征提取步骤。所需的神经网络根据标记的ECG信号学习有目的的特征，然后对这些信号进行分类。分别对39层CNN进行了癫痫发作检测和预测的训练。该方法可以检测癫痫发作，准确率为98.84％，预测准确率为94.29％。通过这种方法，ECG信号可以成为构建便携式系统以监测癫痫患者状态的有前途的指标。
