Machine learning and clinical neurophysiology

Ray, J. J.; Wijesekera, Lokesh; Cirstea, Silvia

doi:10.1007/s00415-022-11283-9

Cited by 4 publications

(3 citation statements)

References 69 publications

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“…Fifty patients were included: 32 good outcomes (22 male; median age [IQR]: 12 [7][8][9][10][11][12][13][14][15][16][17] years) and 18 poor outcomes (8 male; 11 [7-14] years). Table 1 describes the cohort's demographic and clinical information.…”

Section: Patient Cohortmentioning

confidence: 99%

“…Several interictal FC features have been proposed as epileptogenic indicators, but the literature lacks data-driven machine learning (ML) attempts to optimize their use for patient-specific EZ localization when combined with ESI. Although ML has been largely employed on routine scalp EEG to detect or lateralize seizures, [11][12][13] limited attempts have been made to localize the EZ via interictal EEG, especially using FC biomarkers.…”

Section: Introductionmentioning

confidence: 99%

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Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug‐resistant epilepsy: A machine learning approach

Ntolkeras,

Makaram,

Bernabei

et al. 2024

Epilepsia

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ObjectiveTo deconstruct the epileptogenic networks of patients with drug‐resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data.MethodsWe analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non‐epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non‐epileptogenic zones (NEZs) in the same and opposite hemispheres. Region‐specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign‐rank). We developed ML classifiers to identify the VSs in the EZ using VS‐specific FC features. Cross‐validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization.ResultsFC differed between EZ and NEZs (p < .05) during non‐epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non‐epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes.SignificanceWe present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non‐epileptiform epochs. FC‐based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

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Section: Patient Cohortmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug‐resistant epilepsy: A machine learning approach

Ntolkeras,

Makaram,

Bernabei

et al. 2024

Epilepsia

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“…This is why the need for original perspectives of analysis is becoming more pressing. In this regard, the emergence of innovative technologies, such as machine learning (ML), has proven to be a valuable alternative to traditional statistical analyses [1,2,3] .…”

Section: Introductionmentioning

confidence: 99%

Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning analysis

Uceta,

del Cerro-León,

Shpakivska-Bilán

et al. 2024

Preprint

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Background: The demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant changes, not only in neuroanatomical traits, but also in neurophysiological components. One of the most impactful factors we deal with during this time is our environment, especially when encountering external factors such as social behaviors or substance consumption. Binge Drinking (BD) has emerged as an extended pattern of alcohol consumption in teenagers, not only affecting their future lifestyle, but changing their neurodevelopment. Recent studies have changed their scope into finding predisposition factors that may lead adolescents into this kind of patterns of consumption.Methods: In this article, using unsupervised machine learning (UML) algorithms, we analyze the relationship between electrophysiological activity of healthy teenagers and the levels of consumption they had two years later. We used hierarchical agglomerative UML techniques based on Ward's minimum variance criterion to clusterize relations between power spectrum and functional connectivity and alcohol consumption, based on similarity in their correlations, in frequency bands from theta to gamma.Results: We found that all frequency bands studied had a pattern of clusterization based on anatomical regions of interest related to neurodevelopment and cognitive and behavioral aspects of addiction, highlighting the dorsolateral and medial prefrontal, the sensorimotor, the medial posterior and the occipital cortices. All this patterns, of great cohesion and coherence, showed an abnormal electrophysiological activity, representing a dysregulation in the development of core resting-state networks. The clusters found maintained not only plausibility in nature, but robustness, making this a great example of the usage of UML in the analysis of electrophysiological activity, a new perspective into analysis that, while contributing to classical statistics, can clarify new characteristics of the variables of interest.

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E-norms and AI in clinical neurophysiology

Jabre

2024

Clinical Neurophysiology Practice

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Machine learning and clinical neurophysiology

Cited by 4 publications

References 69 publications

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug‐resistant epilepsy: A machine learning approach

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug‐resistant epilepsy: A machine learning approach

Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning analysis

E-norms and AI in clinical neurophysiology

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