Hidden semi-Markov event sequence models: application to brain functional MRI sequence analysis

摘要

Due to the piecewise stationarity assumption required for the observable process of a hidden Markov chain, the application of hidden Markov models (HMMs) to the analysis of event-based random processes remains intricate. For such processes, a new class of HMMs is proposed: the hidden semi-Markov event sequence model (HSMESM). In a HSMESM, the observable process is no more considered as segmental in nature but issued from a detection-characterization preprocessing step. The standard markovian formalism is adapted accordingly. Results obtained in functional MRI sequence analysis validate this novel statistical modeling approach while opening new perspectives in detection-recognition of event-based random processes.