MEG data classification for healthy and epileptic subjects using linear discriminant analysis

Khalid, Muhammad Imran; Aldosari, Saeed A.; Alshebeili, Saleh A.; Alotaiby, Turky N.; Alhameed, Majed; Jad, Lamyaa

doi:10.1109/isspit.2015.7394360

Cited by 7 publications

(6 citation statements)

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“…Sensitivity represents the ratio of number of times the classifier makes correct positive decisions (i.e., detects spikes) to the total number of positive decisions. Specificity is the ratio of number of times the classifier makes correct negative decisions (i.e., detects spike-free segments) to the total number of negative decisions [ 38 ]. After data processing, eight patients were randomly selected to be used in the GP-based feature generation stage.…”

Section: Resultsmentioning

confidence: 99%

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

Alotaiby

Alrshoud

Alshebeili

et al. 2017

Journal of Healthcare Engineering

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Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG) is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP) with the K-nearest neighbor (KNN) for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity.

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Section: Resultsmentioning

confidence: 99%

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

Alotaiby

Alrshoud

Alshebeili

et al. 2017

Journal of Healthcare Engineering

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“…Deep learning will undoubtedly go for better results if there is a sufficient amount of sample data, however at this stage, machine learning can also get good classification results as long as relatively suitable features are extracted. In terms of feature selection, statistical features ( Khalid et al, 2015 ; Alotaiby et al, 2019 ), phase features ( Soriano et al, 2017 ; Matsubara et al, 2018 ; Gautham et al, 2022 ), and graph theoretical features are mainly used for the classification of MEG-based signals ( Wu et al, 2018 ). We can see that a proportion of studies use traditional time-domain signal analysis methods, and over time, more still use frequency-domain and phase-based signal analysis methods or brain signal analysis methods, as these methods better highlight epileptic abnormalities.…”

Section: Discussionmentioning

confidence: 99%

“…In 2015, Kahlid et al first classified epilepsy patients versus healthy subjects using the LDA approach ( Khalid et al, 2015 ). They found that the subjects’ MEG data followed a normal distribution within eight brain regions (right frontal, left frontal, right temporal, left temporal, right parietal, left parietal, right occipital, and left occipital), but with different standard deviations.…”

Section: Methodsmentioning

confidence: 99%

“…The multichannel MEG signal was first segmented into time segments of less than 1 minute, and then eight statistical features (maximum, minimum, mean, standard deviation, median, kurtosis, quartile, and skewness) were extracted from the signal, and the features were fed into an SVM classifier for training, with an average sensitivity and specificity of 99.35 and 95.47% for classification. Using the method on the same dataset (mean sensitivity 100%, mean specificity 90.63%) ( Khalid et al, 2015 ), the sensitivity was found to be almost unchanged, while the specificity was increased by 4.84% ( Alotaiby et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

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Magnetoencephalography-based approaches to epilepsy classification

Pan

Yang

et al. 2023

Front. Neurosci.

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Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.

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“…MEG finds its application in the detection of interictal epileptiform discharges and localizing functional cortices, to guide neurosurgical procedures [ 62 ]. The linear discriminant analysis (LDA) classifier was used for the classification of MEG data obtained from 15 healthy subjects and 18 epilepsy patients [ 63 ]. A two-stage algorithm comprising beamforming by virtual sensors and time-frequency analysis by Stockwell transform was used to detect the high-frequency signals that help in the presurgical planning [ 64 ].…”

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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MEG data classification for healthy and epileptic subjects using linear discriminant analysis

Cited by 7 publications

References 10 publications

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

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

Magnetoencephalography-based approaches to epilepsy classification

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

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