Mark H Myers1, Akaash Padmanabha2, Gavin M Bidelman3, James W Wheless4. 1. Department of Anatomy and Neurobiology, University of Tennessee Health Sciences Center, Memphis, TN, USA. Electronic address: mhmyers99@gmail.com. 2. Department of Chemical Engineering, University of Pennsylvania, Philadelphia, PA, USA. 3. Department of Anatomy and Neurobiology, University of Tennessee Health Sciences Center, Memphis, TN, USA; Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA; School of Communication Sciences & Disorders, University of Memphis, Memphis, TN, USA. 4. Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA.
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.
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:Seizurepatient 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.
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