Clostridium transplantifaecale sp. nov., a new bacterium isolated from patient with recurrent Clostridium difficile infection

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Parabacteroides bouchesdurhonensis sp. nov., a new bacterium isolated from the stool of a healthy adult

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Massilicoli timonensis sp. nov., a new bacterium isolated from the human microbiota

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Genome sequence and description of Bacteroides bouchesdurhonensis sp. nov., a new anaerobic bacterium isolated from the human gut

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Massilimicrobiota timonensis gen. nov., sp. nov., a new bacterium isolated from the human gut microbiota

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Massilistercora timonensis gen. nov., sp. nov., a new bacterium isolated from the human microbiota

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Olsenella timonensis sp. nov., a new bacteria species isolated from the human gut microbiota

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Taxonogenomics description of Bacillus dakarensis sp. nov., Bacillus sinesaloumensis sp. nov. and Bacillus massiliogabonensis sp. nov., three new species isolated from human stools

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Classification model of spikes morphology using principal components analysis in drug-resistant epilepsy

International audienceEpilepsy is one of the diseases that are more subject to consultation in neurological clinics. To help neurologists to accurately diagnose this disease, several technological tools have been developed. Electroencephalography (EEG) of scalp or deep is a signal acquisition tool from electrical discharges of the brain areas. These signals are often accompanied by transient events commonly called interictal paroxystic events (IPE) or spikes of short durations. Analysis of these IPE could help with the diagnosis of drug-resistant epilepsy. With this intention, we will first of all seek to detect IPE, by separating them from the basic activity of signal EEG. In this paper, we propose spike detection method based on Smoothed Nonlinear Energy Operator (SNEO) using adaptive threshold. Then we will implement a new approach using principal components analysis (PCA) before classification to separate the events detected according to their morphologies. The objective in the long term is to characterize their space-time distribution over all the duration of the EEG signal. ©ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018