ArticleScientific reports2020
Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- Prominent astrocytic GLAST pathology occurs in newborn human and piglet hypoxic-ischemic encephalopathy: modeling relationships among laminar neuropathology, seizures, and therapeutic hypothermia.Frontiers in cellular neuroscience · 2026Article
- RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification.Entropy (Basel, Switzerland) · 2025Article
- Machine learning detection of epileptic seizure onset zone from iEEG.Biomedical engineering letters · 2025Review
- Combining meta and ensemble learning to classify EEG for seizure detection.Scientific reports · 2025Article
- Deep Riemannian Networks for end-to-end EEG decoding.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Neural Dynamics Associated with Biological Variation in Normal Human Brain Regions.Entropy (Basel, Switzerland) · 2024Article
- Stereotactic Electroencephalogram Recordings in Temporal Lobectomy Patients Demonstrates the Predictive Value of Interictal Cross-Frequency Correlations: A Retrospective Study.Brain sciences · 2024Article
- Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram.Cognitive neurodynamics · 2023Article
- White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning.Scientific reports · 2023Article
- Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation.Cognitive neurodynamics · 2023Article
- Epileptic seizure focus detection from interictal electroencephalogram: a survey.Cognitive neurodynamics · 2023Review
- Article
- Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG.Bioengineering (Basel, Switzerland) · 2022Article
- Genome sequence analysis of nsp15 from SARS-CoV-2.Bioinformation · 2022Article
- Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation.Brain sciences · 2021Article
- Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis.Sensors (Basel, Switzerland) · 2021Article
- Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy.Entropy (Basel, Switzerland) · 2020Article
- A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control.Sensors (Basel, Switzerland) · 2020Review
- Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography.Sensors (Basel, Switzerland) · 2020Article
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Abstract
Presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. This paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (iEEG). It is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (HFOs) for identifying epileptic focus commonly referred to as the seizure onset zone (SOZ). In this analysis, the multi-channel interictal iEEG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SOZ and non-SOZ channels in iEEG data, the use of machine learning techniques is always tricky. To deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYN) with radial basis function kernel-based SVM was used to detect the focal segments. Finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.
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