ArticleFrontiers in neurology2017
A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy.
Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging.Human brain mapping · 2021Article
- Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning.Scientific reports · 2021Article
- Prediction of Nephrotoxicity Associated With Cisplatin-Based Chemotherapy in Testicular Cancer Patients.JNCI cancer spectrum · 2020Article
- Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.Computational intelligence and neuroscience · 2020Article
- Presurgical epilepsy evaluation and epilepsy surgery.F1000Research · 2019Review
- A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors.Sensors (Basel, Switzerland) · 2018Article
- A MACHINE LEARNING MODEL TO PREDICT SEIZURE SUSCEPTIBILITY FROM RESTING-STATE FMRI CONNECTIVITY.Spring simulation conference (SpringSim)Article
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Authors and funding
4 authors.
Funding
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Abstract
It is crucial to differentiate patients with temporal lobe epilepsy (TLE) from the healthy population and determine abnormal brain regions in TLE. The cortical features and changes can reveal the unique anatomical patterns of brain regions from structural magnetic resonance (MR) images. In this study, structural MR images from 41 patients with left TLE, 34 patients with right TLE, and 58 normal controls (NC) were acquired, and four kinds of cortical measures, namely cortical thickness, cortical surface area, gray matter volume (GMV), and mean curvature, were explored for discriminative analysis. Three feature selection methods including the independent sample
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