{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hinchliffe C"],"funding":["Medical Research Council Clinical Academic Research Partnership award"],"pagination":["1348"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9601450"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(10)"],"pubmed_abstract":["Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM"],"journal":["Entropy (Basel, Switzerland)"],"pubmed_title":["Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures."],"pmcid":["PMC9601450"],"funding_grant_id":["MR/ 208 V037676/1"],"pubmed_authors":["Elkommos S","Hinchliffe C","Yogarajah M","Tang H","Abasolo D"],"additional_accession":[]},"is_claimable":false,"name":"Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures.","description":"Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2025-04-18T16:33:27.621Z","creation":"2025-04-07T03:47:20.015Z"},"accession":"S-EPMC9601450","cross_references":{"pubmed":["37420367"],"doi":["10.3390/e24101348"]}}