Objective Eyelid myoclonia with absences (EMA) is a syndrome characterized by eyelid myoclonia with or without absences, eye closure‐induced generalized electroencephalographic (EEG) paroxysms and photosensitivity. Few data are available about the prognostic factors of this syndrome. The main objectives of our study were to describe the clinical and EEG features of a group of patients with EMA and to evaluate the presence of prognostic factors. Methods We retrospectively selected a cohort of patients with diagnosis of EMA evaluated in the epilepsy service of the Neurological Clinic of Catania, in the Neurology and Clinical Neurophysiopathology Unit of Oasi Research Institute, Troina and in the Regional Epilepsy Centre of Bianchi‐Melacrino‐Morelli Hospital of Reggio Calabria. We considered the features of the patients during the first year of disease, and at the last follow‐up visit. We stratified the patients into two groups: “seizure‐free”, defined as the absence of seizures for at least 2 years, and “not seizure‐free” and we evaluated the evolution of their characteristics and the presence of factors associated with outcome. Results We enrolled 51 patients (40 women (78%); mean age: 30.8 years ± 15.5 [range 10‐79]). The mean follow‐up time was 8.7 ± 5.8 years. Eleven patients (21.6%) achieved the condition of seizure‐free. Family history of epilepsy was associated with the condition of seizure‐free (P = 0.05). At the last follow‐up visit, EEG photosensitivity and eye closure sensitivity were significantly associated with the condition of “not seizure‐free”. Significance The results of our study revealed that a positive family history of epilepsy might be associated with a better outcome in EMA. Furthermore, the persistence of photosensitivity and eye closure sensitivity might indicate persistence of seizures, offering an aid in therapeutic management.

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A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer's Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

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