Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.
Biomed Phys Eng Express. 2024 Feb 29;10(2). doi: 10.1088/2057-1976/ad29a3.
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.
癫痫患者面临的挑战之一是检测癫痫发作的时间并预测其发生的可能性。本研究旨在提供一种基于深度学习的算法，以便在癫痫发作发生前一到两分钟检测和预测其发作时间。所提出的卷积神经网络（CNN）可以通过单导联 ECG 信号处理来检测和预测局灶性癫痫发作的发生，而无需使用 EEG 信号。用于检测和预测癫痫发作的所提出的 CNN 的结构相同。考虑到可穿戴系统的要求，在经过一些轻量级预处理步骤之后，可以直接将 ECG 信号用作神经网络的输入，而无需任何手动特征提取步骤。期望神经网络根据带标签的 ECG 信号学习有针对性的特征，然后对这些信号进行分类。分别对用于癫痫发作检测和预测的 39 层 CNN 进行了训练。该方法可以以 98.84%的准确率检测到癫痫发作，并以 94.29%的准确率对其进行预测。通过这种方法，ECG 信号有望成为构建用于监测癫痫患者状态的便携式系统的指标。
