ArticleEntropy (Basel, Switzerland)2020
Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 23 citations in OpenAlex.
- Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework.Scientific reports · 2026Article
- Predicting Surgical Outcome in Drug-Resistant Epilepsy by Combining Interictal Biomarkers within a Machine Learning Framework.Research square · 2026Article
- TFFBN-HDLF: a hybrid deep learning framework based on time-frequency functional brain networks for epileptic seizure detection.Frontiers in medicine · 2026Article
- Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram.Cognitive neurodynamics · 2023Article
- Epileptic seizure focus detection from interictal electroencephalogram: a survey.Cognitive neurodynamics · 2023Review
- Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.