Spike source identification using artificial intelligence techniques

We present a methodology for the automatic detection of target regions in the brain for ablation, stimulation and restorative surgery for Parkinson´s disease and other neurological disorders. The methodology includes wavelets for the correct characterization of the non-stationarity of the spike train and hidden Markov models as a suitable tool for describing dynamic behavior of the signal across time. Similarity measure and Kullback-Leibler distance were used for discriminant evaluation of HMM. We also compare HMM with other artificial intelligence techniques for the classification task. Results show classification performance up to 97% with the proposed methodology.