Improving classification of epileptic and non-epileptic EEG events by feature selection

This is the Accepted Manuscript version of the following article: E. Pippa, et al, “Improving classification of epileptic and non-epileptic EEG events by feature selection”, Neurocomputing, Vol. 171: 576-585, July 2015. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0925231215009509?via%3Dihub Copyright © 2015 Elsevier B.V.Correctly diagnosing generalized epileptic from non-epileptic episodes, such as psychogenic non epileptic seizures (PNES) and vasovagal or vasodepressor syncope (VVS), despite its importance for the administration of appropriate treatment, life improvement of the patient, and cost reduction for patient and healthcare system, is rarely tackled in the literature. Usually clinicians differentiate between generalized epileptic seizures and PNES based on clinical features and video-EEG. In this work, we investigate the use of machine learning techniques for automatic classification of generalized epileptic and non-epileptic events based only on multi-channel EEG data. For this purpose, we extract the signal patterns in the time domain and in the frequency domain and then combine all features across channels to characterize the spatio-temporal manifestation of seizures. Several classification algorithms are explored and evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting. Due to large number of features feature ranking and selection is performed prior to classification using the ReliefF ranking algorithm within two different voting strategies. The classification models using feature subsets, achieved higher accuracy compared to the models using all features reaching 95% (Bayesian Network), 89% (Random Committee) and 87% (Random Forest) for binary classification (epileptic versus non-epileptic). The results demonstrate the competitiveness of this approach as opposed to previous methods.Peer reviewe

Panayiotopoulos syndrome: a consensus view

The aim of this paper is to promote the correct classification of, and provide guidelines on, the diagnosis and management of panayiotopoulos syndrome (ps). an international consortium of established researchers in the field collaborated to produce a consensus document. the resulting document defines ps, characterizes its electro-clinical features, considers its likely pathogenesis, and provides guidance on appropriate management. we conclude that ps is a common idiopathic, benign seizure disorder of childhood, which should be classified as an autonomic epilepsy, rather than an occipital epilepsy.</p

ELECTROPHYSIOLOGICAL INVESTIGATION OF MITOCHONDRIAL MYOPATHIES

MULTIMODAL EVOKED POTENTIALS AND BLINK REFLEX WERE PERFORMED IN 20 PATIENTS WITH HISTOLOGICALLY, BIOCHEMICALLY AND GENETICALLY CONFIRMED MITOCHONDRIAL MYOPATHY. THE COMBINED EVALUATION OF EVOKED POTENTIAL RESULTS CONFIRMED THE IMPAIRMENT OF THE RELATIVE CENTRAL SENSOR PATHWAYS IN 7 PATIENTS (35%), 4 OF WHOM LACKED CLINICAL EVIDENCE OF CNS INVOLVEMENT. BLINK REFLEX WAS ABNORMAL IN 3 PATIENTS WITH CLINICAL SIGNS OF CNS IMPAIRMENT. INCREASED HABITUATION OF THE BLINK REFLEX WAS OBSERVED IN 11 OUT OF 16 PATIENTS (78.57%). THESE FINDINGS SUGGEST THAT THE BRAINSTEM RETICULAR NETWORK IS IN A STATE OF BASAL INHIBITION WHICH IS PRESSUMABLY IN DUE TO A SUBCLINICAL IMPAIRMENT OF THE CEREBRAL CELLULAR METABOLISM.20 ΑΣΘΕΝΕΙΣ ΜΕ ΙΣΤΟΛΟΓΙΚΑ, ΒΙΟΧΗΜΙΚΑ ΚΑΙ ΓΕΝΕΤΙΚΑ ΔΙΑΓΝΩΣΜΕΝΗ ΜΙΤΟΧΟΝΔΡΙΚΗ ΜΥΟΠΑΘΕΙΑ ΥΠΟΒΛΗΘΗΚΑΝ ΣΕ ΠΛΗΡΗ ΝΕΥΡΟΦΥΣΙΟΛΟΓΙΚΟ ΕΛΕΓΧΟ ΜΕ ΟΠΤΙΚΑ, ΑΚΟΥΣΤΙΚΑ ΚΑΙ ΣΩΜΑΤΟΑΙΣΘΗΤΙΚΑ ΠΡΟΚΛΗΤΑ ΔΥΝΑΜΙΚΑ, ΤΟ ΗΛΕΚΤΡΙΚΑ ΕΚΛΥΟΜΕΝΟ ΑΝΤΑΝΑΚΛΑΣΤΙΚΟ ΣΥΓΚΛΕΙΣΕΩΣ ΤΩΝ ΒΛΕΦΑΡΩΝ ΚΑΙ ΤΗΝ ΕΞΟΙΚΕΙΩΣΗ ΤΟΥ. Η ΜΕΛΕΤΗ ΚΑΙ ΤΩΝ ΤΡΙΩΝ ΣΥΝΟΛΙΚΑ ΤΥΠΩΝ ΤΩΝ ΠΡΟΚΛΗΤΩΝ ΔΥΝΑΜΙΚΩΝ ΕΠΙΒΕΒΑΙΩΣΕ ΤΗ ΔΙΑΤΑΡΑΧΗ ΤΩΝ ΑΝΤΙΣΤΟΙΧΩΝ ΚΕΝΤΡΙΚΩΝ ΑΙΣΘΗΤΙΚΩΝ ΟΔΩΝ ΣΕ 7 ΑΣΘΕΝΕΙΣ (35%) ΑΠΟ ΤΟΥΣ ΟΠΟΙΟΥΣ 4 ΔΕΝ ΠΑΡΟΥΣΙΑΖΑΝ ΚΛΙΝΙΚΕΣ ΕΝΔΕΙΞΕΙΣ ΠΡΟΣΒΟΛΗΣ ΤΟΥ ΚΕΝΤΡΙΚΟΥ ΝΕΥΡΙΚΟΥ ΣΥΣΤΗΜΑΤΟΣ (ΚΝΣ). Η ΜΕΛΕΤΗ ΤΟΥ ΑΝΤΑΝΑΚΛΑΣΤΙΚΟΥ ΣΥΓΚΛΕΙΣΕΩΣ ΤΩΝ ΒΛΕΦΑΡΩΝ ΑΠΟΚΑΛΥΨΕ ΠΑΘΟΛΟΓΙΚΑ ΕΥΡΗΜΑΤΑ ΣΕ 3ΑΣΘΕΝΕΙΣ ΜΕ ΚΛΙΝΙΚΕΣ ΕΝΔΕΙΞΕΙΣ ΒΛΑΒΗΣ ΤΟΥ ΚΝΣ. ΑΥΞΗΜΕΝΗ ΕΞΟΙΚΕΙΩΣΗ ΤΟΥ ΑΝΤΑΝΑΚΛΑΣΤΙΚΟΥ ΣΥΓΚΛΕΙΣΕΩΣ ΤΩΝ ΒΛΕΦΑΡΩΝ ΠΑΡΑΤΗΡΗΘΗΚΕ ΣΕ 11 ΑΠΟ ΤΟΥΣ 16 ΑΣΘΕΝΕΙΣ (78.57%). ΑΥΤΟ ΣΗΜΑΙΝΕΙ ΟΤΙ Ο ΔΙΚΤΥΩΤΟΣ ΣΧΗΜΑΤΙΣΜΟΣ ΤΟΥ ΕΓΚΕΦΑΛΙΚΟΥ ΣΤΕΛΕΧΟΥΣ ΒΡΙΣΚΕΤΑΙ ΜΟΝΙΜΑ ΣΕ ΚΑΤΑΣΤΑΣΗ ΜΕΡΙΚΗΣ ΚΑΤΑΣΤΟΛΗΣ ΠΟΥ ΟΦΕΙΛΕΤΑΙ ΠΙΘΑΝΩΣ ΣΕ ΥΠΟΚΛΙΝΙΚΗ ΔΙΑΤΑΡΑΧΗ ΤΟΥ ΜΕΤΑΒΟΛΙΣΜΟΥ ΤΩΝ ΕΓΚΕΦΑΛΙΚΩΝ ΚΥΤΤΑΡΩΝ

The contribution of the EEG technologists in the diagnosis of Panayiotopoulos syndrome (susceptibility to early onset benign childhood autonomic seizures)

AbstractPurpose: To assess the contribution of the EEG technologists in the diagnosis of children with epileptic seizures. Methods: We analysed the clinical information obtained by the EEG technologists from children with epileptic seizures and their parents, and assessed its value for the generation of a clinically useful EEG report and a plausible electroclinical diagnosis. Interviews were based on a qualitative questionnaire, and were videotaped. We focused on Panayiotopoulos syndrome (PS) because it has a high rate of misdiagnosis, usually for encephalitis or other severe cerebral insults. Results: Between 1998 and 2001, 424 EEG were performed in 308 children aged 1–14 years, of whom 228 (74%) had one or more epileptic seizures. We diagnosed PS in 14 children (6.1%), mainly based on clinical information. Three other had symptomatic ictal vomiting. In 9 of the 14 children with PS, diagnosis was achieved by the information collected by the EEG technologist. Five of these children were being treated for encephalitis, and management was altered accordingly. In a further three children the diagnosis of PS was confirmed. Conclusion: These findings demonstrate that the contribution of the EEG technologists to the diagnosis of people with epilepsies can expand well beyond their established role of recording and describing an EEG. We propose that technologists should be actively involved in prospective electroclinical studies if carefully designed protocols are used