Sleep staging automaton based on the theory of evidence.
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.
Principe
J C
JC
Gala
S K
SK
Chang
T G
TG
D000465:Algorithms; D004569:Electroencephalography; D004576:Electromyography; D004585:Electrooculography; D006801:Humans; D008838:Microcomputers; D008960:Models, Psychological; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*