ArticleScientific reports2023
Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- EEG blink and gaze control using random forest classification for accessible assistive robotic navigation in real world conditions.Scientific reports · 2026Article
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Machine learning-based lateralization and localization of seizure onset in focal cortical dysplasia patients using ictal scalp EEG.Frontiers in neuroscience · 2026Article
- Direct FFT oversampling without zero-padding.Scientific reports · 2025Article
- Improved EEG-Based Emotion Classification via Stockwell Entropy and CSP Integration.Entropy (Basel, Switzerland) · 2025Article
- Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy.Scientific reports · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.
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