Seizure localization using EEG analytical signals. Issue 9 (September 2020)

• Record Type: Journal Article
• Authors: Myers, Mark H.; Padmanabha, Akaash; Bidelman, Gavin M.; Wheless, James W.
• Is Part Of: Clinical neurophysiology. Volume 131:Issue 9(2020:Sep.)
• Page Start: 2131
• Page End: 2139
• Publication Date: 2020-09
• Subjects: Analytic amplitude (AA) -- Analytic phase (AP) -- Seizure onset zone (SOZ) -- Electroencephalograph (EEG); Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2020.05.034
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Continuous monitoring of phase & energy of EEG signals to perform source location. Methodology was found to be robust in locating short seizure instances (8-12 sec). Algorithm achieved 93.3% precision and accuracy and 100% sensitivity. Abstract: Objective: Localization 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. Methods: Seizure 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. Results: Our 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). Conclusions: We 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.