ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2020
Seizure localization using EEG analytical signals.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization.Frontiers in neuroscience · 2024Article
- Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.Biomedicines · 2023Article
- Deep-layer motif method for estimating information flow between EEG signals.Cognitive neurodynamics · 2022Article
- Online Prediction of Lead Seizures from iEEG Data.Brain sciences · 2021Article
- Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals.Frontiers in systems neuroscience · 2021Review
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4 authors.
Funding
Abstract
objectiveLocalization of epileptic seizures, usually characterized by abnormal hypersynchronous wave patterns from the cortex, remains elusive. We present a novel, robust method for automatic localization of seizures on the scalp from clinical electroencephalogram (EEG) data.
methodsSeizure patient EEG data was decomposed via the Hilbert Transform and processed through the following methodology: sorting the analytic amplitude (AA) in the time instance, locating the maximum amplitude within the vector of channels, cross-correlating amplitude values in the time index with the channel vector. The channel with highest AA value in time was located.
resultsOur approach provides an automated way to isolate the epi-genesis of seizure events with 93.3% precision and 100% sensitivity. The method differentiates seizure-related neural activity from other common EEG noise artifacts (e.g., blinks, myogenic noise).
conclusionsWe evaluated performance characteristics of our source location methodology utilizing both phase and energy of EEG signals from patients who exhibited seizure events. Feasibility of the new algorithm is demonstrated and confirmed. SIGNIFICANCE: The proposed method contributes to high-performance scalp localization for seizure events that is more straightforward and less computationally intensive than other methods (e.g., inverse source modeling). Ultimately, it may aid clinicians in providing improved patient diagnosis.
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