Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

Alotaiby, Turky N.; Alrshoud, Saud R.; Alshebeili, Saleh A.; Alhumaid, Majed H.; Alsabhan, Waleed

doi:10.1155/2017/3035606

Cited by 15 publications

(9 citation statements)

References 32 publications

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“…An advantage of the beamformer approach is the method’s ability to typically increase the source SNR compared to that of sensor data [ 19 ]. Although visual data review for the detection of epileptiform activity is still commonly used in the clinical work, with the emergence of reliable methods for automated spike detection [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], the use of visual data review might lose part of its relevance. At this stage, however, where the automated spike detection methods provide suggestions instead of ready solutions, the visual review of data remains an important step in epilepsy analysis.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity of a 29-Channel MEG Source Montage

Nenonen¹,

Helle

Jaiswal

et al. 2022

Brain Sciences

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In this paper, we study the performance of a source montage corresponding to 29 brain regions reconstructed from whole-head magnetoencephalographic (MEG) recordings, with the aim of facilitating the review of MEG data containing epileptiform discharges. Test data were obtained by superposing simulated signals from 100-nAm dipolar sources to a resting state MEG recording from a healthy subject. Simulated sources were placed systematically to different cortical locations for defining the optimal regularization for the source montage reconstruction and for assessing the detectability of the source activity from the 29-channel MEG source montage. The signal-to-noise ratio (SNR), computed for each source from the sensor-level and source-montage signals, was used as the evaluation parameter. Without regularization, the SNR from the simulated sources was larger in the sensor-level signals than in the source montage reconstructions. Setting the regularization to 2% increased the source montage SNR to the same level as the sensor-level SNR, improving the detectability of the simulated events from the source montage reconstruction. Sources producing the SNR of at least 15 dB were visually detectable from the source-montage signals. Such sources are located closer than about 75 mm from the MEG sensors, in practice covering all areas in the grey matter. The 29-channel source montage creates more focal signals compared to the sensor space and can significantly shorten the detection time of epileptiform MEG discharges for focus localization.

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Section: Discussionmentioning

confidence: 99%

Sensitivity of a 29-Channel MEG Source Montage

Nenonen¹,

Helle

Jaiswal

et al. 2022

Brain Sciences

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“…The structural images from imaging modalities, viz., MRI, PET, and SPECT data of the patients, are usually co-registered with MEG for the epilepsy surgery evaluation [ 65 ]. The genetic algorithm with K- nearest neighbor was used for the classification of epileptical MEG spikes [ 66 ]. In [ 67 ], coherence analysis for epilepsy patients was performed on MEG data.…”

Section: Clinical Applicationmentioning

confidence: 99%

A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications

Fred¹,

Kumar²,

Haridhas³

et al. 2022

Brain Sciences

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Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG =2.2 db, SNREEG <1 db) and spatial resolution (SRMEG =2–3 mm, SREEG =7–10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.

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“…Therefore, an automated and effective solution is needed to foster efficient and unbiased use of MEG in routine clinical practice for epilepsy, and AI has shown the potential to address these needs. Although the feasibility of applying AI algorithms to the identification of interictal EDs (IEDs) to increase clinical efficiency has been demonstrated [31][32][33] and recently improved using deep learning frameworks [34,35], these studies were designed only to optimise a few steps of the clinical analysis. Additionally, several other studies proposed novel clustering algorithms based on unsupervised learning by AI to improve the signal-to-noise ratio (SNR) of IEDs [36][37][38] or developed deep learning-based source estimation methods to localise IEDs more accurately [39][40][41] but failed to demonstrate how these approaches might translate from bench to clinic.…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence–based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography

Zheng,

Liao,

et al. 2023

J. Neural Eng.

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Objective. Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence–based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data. Approach. To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients. Main results. The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h). Significance. The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.

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Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

Cited by 15 publications

References 32 publications

Sensitivity of a 29-Channel MEG Source Montage

Sensitivity of a 29-Channel MEG Source Montage

A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications

An artificial intelligence–based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography

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