Adaptive architecture for medical application case study: Evoked Potential detection using matching poursuit consensus

The emergency of embedded systems puts new challenges for the design of different system in many fields. One of the embedded application's fields is the medical one. The major difficulty is the embedded system's reduced energy and computational resources that must be carefully used to execute complex application often in unpredictable environments. In this paper, the used application is the detection of evoked potential with variable latency and multiple trials using consensus matching pursuit. Fitting to the noisy Evoked Potential (EP) signal persistent in all response, we use the Consensus version of the matching pursuit algorithm (CMP). EP is a resulted wave from a stimulus. The EP can be explained with a good quality of energy ratio factor (QR). If we use a noisy EP, we cannot reconstruct the original data because of the random atoms of CMP dictionary. We select the significant atoms to rebuild and EP signals. This application is embedded on a Xilinx ML 507. We used an adaptive architecture based on dynamically partial reconfiguration.