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Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy

Epilepsy is one of the most common brain disorders worldwide. Poststroke epilepsy (PSE) affects functional retrieval after stroke and brings considerable social values. A stroke occurs when the blood circulation to the brain fails, causing speech difficulties, memory loss, and paralysis. An electroe...

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Autores principales: Shen, Deju, Deng, Yuqin, Lin, Chunyan, Li, Jianshu, Lin, Xuehua, Zou, Chaoning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444425/
https://www.ncbi.nlm.nih.gov/pubmed/36105439
http://dx.doi.org/10.1155/2022/4801037
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author Shen, Deju
Deng, Yuqin
Lin, Chunyan
Li, Jianshu
Lin, Xuehua
Zou, Chaoning
author_facet Shen, Deju
Deng, Yuqin
Lin, Chunyan
Li, Jianshu
Lin, Xuehua
Zou, Chaoning
author_sort Shen, Deju
collection PubMed
description Epilepsy is one of the most common brain disorders worldwide. Poststroke epilepsy (PSE) affects functional retrieval after stroke and brings considerable social values. A stroke occurs when the blood circulation to the brain fails, causing speech difficulties, memory loss, and paralysis. An electroencephalogram (EEG) is a tool that may detect anomalies in brain electrical activity, including those induced by a stroke. Using EEG data to determine the electrical action in the brains of stroke patients is an effort to measure therapy. Hence in this paper, deep learning assisted gene mutation analysis (DL-GMA) was utilized for classifying poststroke epilepsy in patients. This study suggested a model categorizing poststroke patients based on EEG signals that utilized wavelet, long short-term memory (LSTM), and convolutional neural networks (CNN). Gene mutation analysis can help determine the cause of an individual's epilepsy, leading to an accurate diagnosis and the best probable medical management. The test outcomes show the viability of noninvasive approaches that quickly evaluate brain waves to monitor and detect daily stroke diseases. The simulation outcomes demonstrate that the proposed GL-GMA achieves a high accuracy ratio of 98.3%, a prediction ratio of 97.8%, a precision ratio of 96.5%, and a recall ratio of 95.6% and decreases the error rate 10.3% compared to other existing methods.
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spelling pubmed-94444252022-09-13 Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy Shen, Deju Deng, Yuqin Lin, Chunyan Li, Jianshu Lin, Xuehua Zou, Chaoning Contrast Media Mol Imaging Research Article Epilepsy is one of the most common brain disorders worldwide. Poststroke epilepsy (PSE) affects functional retrieval after stroke and brings considerable social values. A stroke occurs when the blood circulation to the brain fails, causing speech difficulties, memory loss, and paralysis. An electroencephalogram (EEG) is a tool that may detect anomalies in brain electrical activity, including those induced by a stroke. Using EEG data to determine the electrical action in the brains of stroke patients is an effort to measure therapy. Hence in this paper, deep learning assisted gene mutation analysis (DL-GMA) was utilized for classifying poststroke epilepsy in patients. This study suggested a model categorizing poststroke patients based on EEG signals that utilized wavelet, long short-term memory (LSTM), and convolutional neural networks (CNN). Gene mutation analysis can help determine the cause of an individual's epilepsy, leading to an accurate diagnosis and the best probable medical management. The test outcomes show the viability of noninvasive approaches that quickly evaluate brain waves to monitor and detect daily stroke diseases. The simulation outcomes demonstrate that the proposed GL-GMA achieves a high accuracy ratio of 98.3%, a prediction ratio of 97.8%, a precision ratio of 96.5%, and a recall ratio of 95.6% and decreases the error rate 10.3% compared to other existing methods. Hindawi 2022-08-29 /pmc/articles/PMC9444425/ /pubmed/36105439 http://dx.doi.org/10.1155/2022/4801037 Text en Copyright © 2022 Deju Shen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Shen, Deju
Deng, Yuqin
Lin, Chunyan
Li, Jianshu
Lin, Xuehua
Zou, Chaoning
Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy
title Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy
title_full Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy
title_fullStr Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy
title_full_unstemmed Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy
title_short Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy
title_sort clinical characteristics and gene mutation analysis of poststroke epilepsy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444425/
https://www.ncbi.nlm.nih.gov/pubmed/36105439
http://dx.doi.org/10.1155/2022/4801037
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