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A number of stochastic models and statistical tests are synthesised to develop a general framework for signal analysis and classification. The particular application which provides a focus for this work is the automatic real-time analysis and classification of human electroencephalograms as a clinical aid to diagnosis, treatment and long term monitoring of epileptic patients. Stochastic modelling and estimation procedures are described; these procedures can be employed together with previously recorded data to determine signal classes which are differentiated on the basis of their first and second order moments. Since nearly all analyses of the electroencephalogram study only these moments, and because these moments have been demonstrated to be reliable indicators of the physiological condition, it is expected that the resulting signal classes will be clinically meaningful. It is shown that standard methods of statical hypothesis testing can be used to classify segments of the electroencephalographic record, various approximations are introduced, including a hierarchical test procedure, to develop suboptimal but computationally efficient classification procedures. The use of expert judgement to relate these stochastically differentiated signal classes to the answers to clinically meaningful questions is also discussed; this relationship then permits the system to provide its classification results in a clinically meaningful form.

A number of stochastic models and statistical tests are synthesised to develop a general framework for signal analysis and classification. The particular application which provides a focus for this work is the automatic real-time analysis and classification of human electroencephalograms as a clinical aid to diagnosis, treatment and long term monitoring of epileptic patients. Stochastic modelling and estimation procedures are described; these procedures can be employed together with previously recorded data to determine signal classes which are differentiated on the basis of their first and second order moments. Since nearly all analyses of the electroencephalogram study only these moments, and because these moments have been demonstrated to be reliable indicators of the physiological condition, it is expected that the resulting signal classes will be clinically meaningful. It is shown that standard methods of statical hypothesis testing can be used to classify segments of the electroencephalographic record, various approximations are introduced, including a hierarchical test procedure, to develop suboptimal but computationally efficient classification procedures. The use of expert judgement to relate these stochastically differentiated signal classes to the answers to clinically meaningful questions is also discussed; this relationship then permits the system to provide its classification results in a clinically meaningful form.

Neurological manifestations associated with influenza virus infection include encephalitis, encephalopathy, acute necrotizing encephalitis, transverse myelitis, acute disseminated encephalomyelitis, mild encephalitis with reversible splenial syndrome (MERS), and Guillaín Barré syndrome. We report a 16-year-old female who was admitted at our emergency department with seizures, confusion, nystagmus and motor clumsiness five days after an upper a respiratory tract infection. Influenza type B virus infection was confirmed by chain polymerase reaction analysis. The initial electroencephalogram demonstrated a pattern of global slowness without epileptic discharges. One week later, it showed a progression to slow-wave focal bilateral discharges at both temporal and occipital lobes. The patient had a favorable evolution and was discharged 19 days after admission with phenytoin to prevent seizures.

The differential diagnosis of non-convulsive status epilepticus (NCSE) is often complex due to a wide clinical variability of its presentation, including psychiatric manifestations. We report a 68 years old male with a history of depression treated with venlafaxine, mirtazapine, quetiapine and risperidone, presenting in the emergency room with confusion and generalized rigidity. A brain CT scan did not show lesions. A neuroleptic syndrome was initially suspected. At the third day the obtundation worsened and an electroencephalogram (EEG) was performed, which showed epileptiform abnormalities. Treatment with valproic acid resulted in disappearance of such abnormalities. After three weeks of mechanical ventilation, the patient was extubated and remained lucid and partially orientated in time and space.

Creutzfeldt-Jakob disease has a higher incidence in Chile than in other countries. The post mortem pathological characterization of brain tissue is necessary to reach a definitive diagnosis. We report a 73 years old man with a history compatible with of a rapidly progressive dementia, in which the first electroencephalographic study showed a pattern consistent with non-convulsive status epilepticus. Besides discarding this diagnosis, it was necessary to rule out other causes of rapidly progressive dementia such as Hashimoto encephalopathy. Finally, the sustained clinical deterioration with no response to anticonvulsants and corticosteroids, the imaging studies, a serial electroencephalographic monitoring study and the detection of 14-3-3 protein in cerebrospinal fluid were the keys to achieve the diagnosis of the disease.

Dans ce document, nous décrivons une expérience dont les données ont été publiées sur https://doi.org/10.5281/zenodo.2348891 aux formats mat (Mathworks, Natick, USA) and csv. Cette base de données contient les enregistrements électroencéphalographiques de 20 sujets au repos avec les yeux ouverts ou fermés. Le casque d’électroencéphalographie comportait 16 électrodes. Les données ont été enregistrées durant une expérience pilote, qui s'est déroulée au GIPSA-lab, Grenoble, France en 2017 (1). Nous fournissons également une implémentation en Python pour manipuler les données sous https://github.com/plcrodrigues/Alpha-Waves-Dataset. L'identifiant de cette base de donnée est ALPHA.EEG.2017-GIPSA. We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2348891 in both mat (Mathworks, Natick, USA) and csv formats. This dataset contains electroencephalographic recordings of 20 subjects in a simple resting-state eyes open/closed experimental protocol. The electroencephalographic headset consisted of 16 electrodes. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017 (1). Python code for manipulating the data is available at https://github.com/plcrodrigues/Alpha-Waves-Dataset . The ID of this dataset is ALPHA.EEG.2017a-GIPSA.

Abstract : One male rhesus monkey (Macaca mulatta) was exposed to NOSET-A aerosol for 4 hours at a concentration of 2.4 ppm. Visual evoked response and Sidman avoidance task (free operant avoidance) data were collected after 2 hours and 4 hours of exposure. The visual evoked response was not affected, but a significant increase in response rate on the Sidman avoidance task occurred.