Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.
For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.
Schwaibold
M
M
FZI Forschungszentrum Informatik, Karlsruhe, Germany. schwaibold@fzi.de
Schöchlin
J
J
Bolz
A
A
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D017286:Polysomnography / Q000295:instrumentation*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012894:Sleep Stages / Q000502:physiology*