Classification of EEG Signal-Based Encephalon Magnetic Signs for Identification of Epilepsy-Based Neurological Disorder

Abstract

Magnetoencephalography (MEG) is now widely used in clinical examinations and medical research in many fields. Resting-state magnetoencephalography-based brain network analysis can be used to study the physiological or pathological mechanisms of the brain. Furthermore, magnetoencephalography analysis has a significant reference value for the diagnosis of epilepsy. The scope of the proposed research is that this research demonstrates how to locate spikes in the phase locking functional brain connectivity network of the Desikan-Killiany brain region division using a neural network approach. It also improves detection accuracy and reduces missed and false detection rates. The automatic classification of epilepsy encephalomagnetic signals can make timely judgments on the patient’s condition, which is of tremendous clinical significance. The existing literature’s research on the automatic type of epilepsy EEG signals is relatively sufficient, but the research on epilepsy EEG signals is relatively weak. A full-band machine learning automatic discrimination method of epilepsy brain magnetic spikes based on the brain functional connection network is proposed. The four classifiers are comprehensively compared. The classifier with the best effect is selected, and the discrimination accuracy can reach 93.8%. Therefore, this method has a good application prospect in automatically identifying and labeling epileptic spikes in magnetoencephalography.

Magnetoencephalography-based approaches to epilepsy classification

“…Similar to spike detection, HFO detection is also divided into three steps: (i) first pre-processing the signal; (ii) then pre-detecting possible HFOs; and (iii) classifying previously detected candidate events in order to distinguish real HFOs from artefacts and noise in the signal. In terms of feature selection, similar to the use of MEG signals for epilepsy detection and classification, statistical features ( Alotaiby et al, 2017 ; Khalid et al, 2017 ; Sdoukopoulou et al, 2021 ), phase features ( Kaur et al, 2022 ), graph theoretic/networks ( Sdoukopoulou et al, 2021 ) and other features have been explored in the time and frequency domains. Overall, the detection of epileptogenic foci will be facilitated by the use of CAD methods for the detection of abnormal signals in epilepsy.…”

“…Sdoukopoulou et al considered a combined MEG and EEG approach to develop a multi-feature and iterative classification scheme and achieved a classification result of recall 90.2%, specificity 95.1%, and accuracy 92.8% ( Sdoukopoulou et al, 2021 ). Kaur et al proposed a strategy of locating spikes in the phase locking functional brain connectivity network using a machine learning method which achieved a classification accuracy of up to 93.8% ( Kaur et al, 2022 ).…”

Retracted: Classification of EEG Signal‐Based Encephalon Magnetic Signs for Identification of Epilepsy‐Based Neurological Disorder

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”