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arXiv · 2510.15717

Automated detection of circadian-dependent epileptic biomarkers for seizure localization using machine learning and signal processing

Mehdi Zekriyapanah Gashti·Mostafa Mohammadpour·Hassan Eshkiki·Vahid Ghanbarizadeh
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Abstract

Accurate localization of the seizure onset zone (SOZ) is essential for successful epilepsy surgery, yet the reliability of commonly used interictal biomarkers is limited by temporal variability and behavioral state. This study aims to investigate the circadian and sleep-dependent dynamics of epileptic biomarkers and to identify conditions that maximize seizure localization precision. Longterm intracranial EEG recordings from nine patients with drug-resistant focal epilepsy were retrospectively analyzed using automated signal processing and machine learning techniques. Interictal spikes, spike sequences, high-frequency oscillations (HFOs), and pathological HFOs were automatically detected, while sleep and wake states were classified using the alpha-delta power ratio. Biomarker rates, spatial distributions, and localization accuracy were quantitatively evaluated using Euclidean distance relative to the clinically defined SOZ. The results show that all biomarkers exhibit significantly higher rates during sleep, with pronounced early-morning peaks. Importantly, spike sequences and pathological HFOs demonstrated superior spatial precision compared to conventional spikes or HFOs alone. Mean distances to the SOZ were substantially lower for pathological HFOs and spike sequences, with statistically significant differences among biomarkers (ANOVA, p < 0.001). These findings demonstrate that sleep-state analysis, particularly using propagated spike sequences and pathological HFOs, substantially improves SOZ localization accuracy. The proposed framework provides practical guidance for sleep-focused presurgical EEG analysis and supports the development of automated and clinically efficient seizure localization systems.

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Mehdi Zekriyapanah Gashti, Mostafa Mohammadpour, Hassan Eshkiki, Vahid Ghanbarizadeh. 2025-10-17. Automated detection of circadian-dependent epileptic biomarkers for seizure localization using machine learning and signal processing. https://doi.org/10.18488/76.v13i2.4981

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