TY - GEN
T1 - Recognition of brain structures from MER-signals using dynamic MFCC analysis and a HMC classifier
AU - Holguin, Mauricio
AU - Holguin, German A.
AU - Cardona, Hernán Darío Vargas
AU - Daza, Genaro
AU - Guijarro, Enrique
AU - Orozco, Alvaro
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Dynamic features
KW - Hidden Markov Chain (HMC)
KW - MEL-Frequency cepstral coefficients (MFCC)
KW - MER signals
KW - Parkinson's disease
UR - https://www.scopus.com/pages/publications/84891284216
U2 - 10.1007/978-3-319-00846-2_184
DO - 10.1007/978-3-319-00846-2_184
M3 - Conference contribution
AN - SCOPUS:84891284216
SN - 9783319008455
T3 - IFMBE Proceedings
SP - 742
EP - 745
BT - 13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013 - MEDICON 2013
PB - Springer Verlag
T2 - 13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013
Y2 - 25 September 2013 through 28 September 2013
ER -