ArticleTheScientificWorldJournal2014
Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.
Article in TheScientificWorldJournal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 34 citations in OpenAlex.
- Study of driving fatigue alleviation by transcutaneous acupoints electrical stimulations.TheScientificWorldJournal · 2014Trial
- Trial
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- EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.Sensors (Basel, Switzerland) · 2019Review
- EEG characteristic analysis of coach bus drivers based on brain connectivity as revealedRSC advances · 2018Article
- Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy.Entropy (Basel, Switzerland) · 2018Article
- EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots.Frontiers in human neuroscience · 2018Review
- Use of ADMS™ during sedation for dental treatment of an intellectually disabled patient: a case report.Journal of dental anesthesia and pain medicine · 2016Article
- Diagnosing Parkinson's Diseases Using Fuzzy Neural System.Computational and mathematical methods in medicine · 2016Article
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.
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