[Retracted] Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy

Abstract

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.

Deep Learning Aided Neuroimaging and Brain Regulation

“…A deep learning model was developed for real-time decoding of MEG signals, which was applied to a brain-computer interface system for regulating motor imagery tasks [96]. Epilepsy is a chronic brain disorder in which functional changes may precede structural ones and which may be detectable using existing modalities [97]. Functional connectivity analysis using EEG and resting state-functional magnetic resonance imaging (rs-fMRI) can localize epilepsy [98,99].…”

Applications for Deep Learning in Epilepsy Genetic Research

“…The model architecture was designed to detect complex nonlinear relationships between SNV and the image data. In epilepsy, Shen et al [22], used a convolutional neural network (CNN) to predict post-stroke epilepsy. The model inputs include EEG signal data and the frequency of gene mutations in genes associated with stroke.…”

Retracted: Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”