Identification of spike sources using proximity analysis through hidden Markov models.
Hidden Markov models have shown promising results for identification of spike sources in Parkinson's disease treatment, e.g., for deep brain stimulation. Usual classification criteria consist in maximum likelihood rule for the recognition of the correct class. In this paper, we present a different classification scheme based in proximity analysis. For this approach matrices of Markov process are transformed to another space where similarities and differences to other Markov processes are better revealed. The authors present the proximity analysis approach using hidden Markov models for the identification of spike sources (Thalamo and Subthalamo sources, Gpi and GPe sources). Results show that proximity analysis improves recognition performance for about 5% over traditional approach.
Orozco
Alvaro
A
Programa de Ingeniería Eléctrica, Universidad Tecnológica de Pereira, La Julita, Colombia. aaog@utp.edu.co
Alvarez
Mauricio
M
Guijarro
Enrique
E
Castellanos
Germán
G
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D046690:Deep Brain Stimulation; D016002:Discriminant Analysis; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009716:Numerical Analysis, Computer-Assisted; D010300:Parkinson Disease / Q000473:pathology*; D010363:Pattern Recognition, Automated; D013269:Stochastic Processes