Studying of deep neural networks and delta and alpha sub-bands harmony signals for Prediction of epilepsy

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biomedical Signal Processing and Control Pub Date : 2024-10-29 DOI:10.1016/j.bspc.2024.107066
G. Alizadeh, T. Yousefi Rezaii, S. Meshgini
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Abstract

Epilepsy, a seizure disorder, is one of the significant diseases in the global community. More than 1% of the world’s population is affected by this disease. It is controlled with medicine in a mild case. Neurologists use Electroencephalography (EEG) to diagnose epilepsy in most medical centers and hospitals. In recent years, researchers have conducted numerous studies to estimate epilepsy attacks using EEG. In this study, a new method is presented to enhance the accuracy, sensitivity, and other necessary parameters for estimating epilepsy attacks. In the proposed algorithm, the processing of brain signals is performed in two stages. In the first stage, the brain signals are decomposed into delta, theta, beta and alpha sub-bands using Discrete Wavelet Transform (DWT). Subsequently, the accuracy of the sub-bands are analyzed using a Long Short-Term Memory Neural Network (LSTM). Sub-bands with an accuracy of over 70% are selected for the second stage. In the second processing stage, selected sub-band harmonic signal images are used as input for a convolutional neural network (CNN) to extract features and make the final decision. The use of the proposed method results in an improvement in all parameters for estimating epilepsy attacks, including accuracy, sensitivity, and AUC. The results of the proposed method show a 45% increase compared to the conventional method.

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研究深度神经网络与德尔塔和阿尔法子波段和谐信号对癫痫的预测作用
癫痫是一种发作性疾病,是全球重大疾病之一。全世界有超过 1%的人口受到这种疾病的影响。轻度患者可以通过药物控制病情。大多数医疗中心和医院的神经科医生都使用脑电图(EEG)来诊断癫痫。近年来,研究人员进行了大量研究,利用脑电图估计癫痫发作。本研究提出了一种新方法,以提高估计癫痫发作的准确性、灵敏度和其他必要参数。在所提出的算法中,大脑信号的处理分两个阶段进行。在第一阶段,使用离散小波变换(DWT)将脑信号分解为 delta、theta、beta 和 alpha 子带。随后,使用长短期记忆神经网络(LSTM)分析子波段的准确性。准确率超过 70% 的子带将被选入第二阶段。在第二处理阶段,选定的子带谐波信号图像被用作卷积神经网络(CNN)的输入,以提取特征并做出最终决定。使用所提出的方法可以改善癫痫发作估计的所有参数,包括准确性、灵敏度和 AUC。与传统方法相比,建议方法的结果显示提高了 45%。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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