Stereo-electroencephalography （SEEG） is the main investigation method for pre-surgical evaluation of patients suffering from drug-resistant partial epilepsy. SEEG signals reflect two types of paroxysmal activity： i...Stereo-electroencephalography （SEEG） is the main investigation method for pre-surgical evaluation of patients suffering from drug-resistant partial epilepsy. SEEG signals reflect two types of paroxysmal activity： ictal activity and interictal activity or interictal spikes （IS）. The relationship between IS and ictal activity is an essential and recurrent question in epiletology. In this paper, we present a distributed and parallel architecture for space and temporal distribution analysis of IS, based on a distributed and collaborative methodology. The proposed approach exploits the SEEG data using vector analysis of the corresponding signals among multi-agents system. The objective is to present a new method to analyze and classify IS during wakefulness （W）, light sleep （LS） and deep sleep （DS） stages. Temporal and spatial relationships between IS and seizure onset zone are compared during wakefulness, light sleep and deep sleep. Results show that space and temporal distribution for real data are not random but correlated.展开更多
文摘Stereo-electroencephalography （SEEG） is the main investigation method for pre-surgical evaluation of patients suffering from drug-resistant partial epilepsy. SEEG signals reflect two types of paroxysmal activity： ictal activity and interictal activity or interictal spikes （IS）. The relationship between IS and ictal activity is an essential and recurrent question in epiletology. In this paper, we present a distributed and parallel architecture for space and temporal distribution analysis of IS, based on a distributed and collaborative methodology. The proposed approach exploits the SEEG data using vector analysis of the corresponding signals among multi-agents system. The objective is to present a new method to analyze and classify IS during wakefulness （W）, light sleep （LS） and deep sleep （DS） stages. Temporal and spatial relationships between IS and seizure onset zone are compared during wakefulness, light sleep and deep sleep. Results show that space and temporal distribution for real data are not random but correlated.
