Sleep staging automaton based on the theory of evidence

Abstract

This paper addresses sleep staging as a medical decision problem. It develops a model for automated sleep staging by combining signal information, human heuristic knowledge in the form of rules, and a mathematical framework. The EEG/EOG/EMG events relevant for sleep staging are detected in real time by an existing front-end system and are summarized per minute. These token data are translated, normalized, and constitute the input alphabet to a finite state machine (automaton). The processed token events are used as partial belief in a set of anthropomimetic rules, which encode human knowledge about the occurrence of a particular sleep stage. The Dempster-Shafer theory of evidence weighs the partial beliefs and attributes the minutes sleep stage to the machine state transition that displays the highest final belief. Results are briefly presented.