A dictionary learning approach for spatio-temporal characterization of absence seizures

International audienceObjective: This research explores absence seizures using data recorded from different layers of somatosensory cortex of four genetic absence epilepsy rats from Strasbourg (GAERS). Localizing the active layers of somatosensory cortex (spatial analysis) and investigating the dynamics of recorded seizures (temporal analysis) are the main goals of this research. Approach: We model the spike discharges of seizures using a generative spatio-temporal model. We assume that there are some states under first-order Markovian model during seizures, and each spike is generated when the corresponding state is activated. We also assume that a few specific epileptic activities (or atoms) exist in each state which are linearly combined and form the spikes. Each epileptic activity is described by two characteristics: (1) its spatial topography which shows the organization of current sources and sinks generating the epileptic activity, and (2) its temporal representation which illustrates the activation function of the epileptic activity. We show that the estimation of the model parameters, i.e. states and their epileptic activities (atoms), is similar to solving a dictionary learning problem for sparse representation. Instead of using classical dictionary learning algorithms, a new approach, taking into account the Markovian nature of the model, is proposed for estimating the models parameters, and its efficiency is experimentally verified. Main results: Experimental results show that there are one dominant and one unstable state with two epileptic activities in each during the seizures (temporal analysis). It is also found that the top and bottom layers of the somatosensory cortex are the most active layers during seizures (spatial analysis). The structural model is similar for all rats with a spatial topography which is the same for all rats but a temporal activation which changes according to the rat. Significance: The proposed framework can be applied on any database acquired from a small area of the brain, and can provide valuable spatio-temporal analysis for neuroscientists