{"title":"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","authors":"G. Alizadeh, T. Yousefi Rezaii, S. Meshgini","doi":"10.1016/j.bspc.2024.107066","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Studying of deep neural networks and delta and alpha sub-bands harmony signals for Prediction of epilepsy\",\"authors\":\"G. Alizadeh, T. Yousefi Rezaii, S. Meshgini\",\"doi\":\"10.1016/j.bspc.2024.107066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55362,\"journal\":{\"name\":\"Biomedical Signal Processing and Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Signal Processing and Control\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1746809424011248\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424011248","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Studying of deep neural networks and delta and alpha sub-bands harmony signals for Prediction of epilepsy
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.
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.