A novel methodology for the characterization of Microelectrode Recording signals (MER-signals) in Parkinson's patients in order to recognize basal ganglia in the brain is presented in this work. The most common approach of MER signals analysis consists of time-frequency analysis through Short Time Fourier Transform, Wavelet Transform, or Filters Banks. We present an approach based on MEL-Frequency Cepstral Coefficients (MFCC) and K-means clustering to obtain dynamic features from MER-signals. A Hidden Markov Chain (HMC) with 1, 2, 3, and 4 states was used for the classification of four classes of basal ganglia: Thalamus (Tal), Zone Incerta (ZI), Subthalamic Nucleus (STN) and Substantia Nigra reticulata (SNr), achieving a positive identification over 82%. A performance analysis for each HHM model is presented using ROC curves. © Springer International Publishing Switzerland 2014.
CITATION STYLE
Holguin, M., Holguin, G. A., Cardona, H. D. V., Daza, G., Guijarro, E., & Orozco, A. (2014). Recognition of brain structures from MER-signals using dynamic MFCC analysis and a HMC classifier. In IFMBE Proceedings (Vol. 41, pp. 742–745). Springer Verlag. https://doi.org/10.1007/978-3-319-00846-2_184
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