Hidden process modeling

Presents a method that is a generalization of hidden Markov modeling for the situations where elementary events cannot be clearly defined. A family of fuzzy sets, induced on a temporal universe, is used to model the dynamic trajectory of a physical system as a collection of hidden processes that coexist at the same time, but to different degrees. An algorithm based on unsupervised pattern recognition that estimates the prototypes and activities of the hidden processes is presented. The performance of the method is illustrated using experimental data obtained from electroencephalographic (EEG) signals recorded during sleep