Adaptive hybrid system for automatic sleep staging.
We present a new adaptive system for automated sleep staging. The proposed system relies on each subject's own data for self-training. Conventional automatic sleep staging algorithms are either rule based, which typically fail to accurately model the complex nature of sleep signals, or numerical methods that use multi-patient training schemes, which suffer from inaccuracies caused by inherent inter-patient variability. The proposed system employs two stages. The first stage is a rule based reasoning engine that can be tuned conservatively to decrease or eliminate false positives, generating just enough samples to train the second stage, which is comprised of a neural network classifier. Results show that this hybrid approach provides an adaptive training scheme that performs more accurately compared to one of the popular commercially available systems.
Hassaan
Amr A
AA
Department of Systems and Biomedical Engineering, Cairo University, Egypt.
Morsy
Ahmed A
AA
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001698:Biomedical Engineering; D004569:Electroencephalography / Q000706:statistics & numerical data; D004585:Electrooculography / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*; D012895:Sleep, REM / Q000502:physiology