卷积神经网络
癫痫
模式识别(心理学)
信号(编程语言)
计算机科学
铅(地质)
人工智能
癫痫发作
神经科学
心理学
生物
古生物学
程序设计语言
作者
Mostafa Ghaempour,Kourosh Hassanli,Ebrahim Abiri
出处
期刊:Biomedical Physics & Engineering Express
[IOP Publishing]
日期:2024-02-15
卷期号:10 (2): 025041-025041
被引量:1
标识
DOI:10.1088/2057-1976/ad29a3
摘要
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.
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