To evaluate the clinical associations of adult‐onset photosensitivity, we studied the clinical and EEG data of patients who were referred due to a possible first seizure and who had a photoparoxysmal response on their EEG. Patients with clinical evidence of photosensitivity before the age of 20 were excluded. Of a total of 30 patients, four had acute symptomatic seizures, two had vasovagal syncope, and 24 were diagnosed with epilepsy. Nine of the 24 patients had idiopathic (genetic) generalized epilepsies and predominantly generalized photoparoxysmal response, but also rare photically‐induced seizures, while 15 had exclusively, or almost exclusively, reflex photically‐induced occipital seizures with frequent secondary generalization and posterior photoparoxysmal response. Other important differences included a significantly older age at seizure onset and paucity of spontaneous interictal epileptic discharges in patients with photically‐induced occipital seizures; only a quarter of these had occasional occipital spikes, in contrast to the idiopathic (genetic) generalized epilepsy patients with typically generalized epileptic discharges. On the other hand, both groups shared a positive family history of epilepsy, common seizure threshold modulators (such as tiredness and sleep deprivation), normal neurological examination and MRI, a generally benign course, and good response to valproic acid. We demonstrated that photosensitivity can first occur in adult life and manifest, either as idiopathic (possibly genetic) photosensitive occipital epilepsy with secondary generalization or as an EEG, and less often, a clinical/EEG feature of idiopathic (genetic) generalized epilepsies. Identification of idiopathic photosensitive occipital epilepsy fills a diagnostic gap in adult first‐seizure epileptology and is clinically important because of its good response to antiepileptic drug treatment and fair prognosis.

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

This is the accepted manuscript version of the following article: Iosif Mporas, ???Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients???, Expert Systems with Applications, Vol. 42(6), December 2014. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0957417414007763?via%3Dihub ?? 2014 Elsevier Ltd. All rights reserved.In this paper a seizure detector using EEG and ECG signals, as a module of a healthcare system, is presented. Specifically, the module is based on short-time analysis with time-domain and frequency-domain features and classification using support vector machines. The seizure detection module was evaluated on three subjects with diagnosed idiopathic generalized epilepsy manifested with absences. The achieved seizure detection accuracy was approximately 90% for all evaluated subjects. Feature ranking investigation and evaluation of the seizure detection module using subsets of features showed that the feature vector composed of approximately the 65%-best ranked parameters provides a good trade-off between computational demands and accuracy. This configurable architecture allows the seizure detection module to operate as part of a healthcare system in offline mode as well as in online mode, where real-time performance is needed