Autoassociative MLP in sleep spindle detection.
Spindles are one of the most important short-lasting waveforms in sleep EEG. They are the hallmarks of the so-called Stage 2 sleep. Visual spindle scoring is a tedious workload, since there are often a thousand spindles in one all-night recording of some 8 hr. Automated methods for spindle detection typically use some form of fixed spindle amplitude threshold, which is poor with respect to inter-subject variability. In this work a spindle detection system allowing spindle detection without an amplitude threshold was developed. This system can be used for automatic decision making of whether or not a sleep spindle is present in the EEG at a certain point of time. An Autoassociative Multilayer Perceptron (A-MLP) network was employed for the decision making. A novel training procedure was developed to remove inconsistencies from the training data, which was found to improve the system performance significantly.
Huupponen
E
E
Signal Processing Laboratory, Tampere University of Technology, Finland.
Värri
A
A
Himanen
S L
SL
Hasan
J
J
Lehtokangas
M
M
Saarinen
J
J
D000328:Adult; D000513:Alpha Rhythm / Q000145:classification; D016477:Artifacts; D001331:Automation; D001611:Beta Rhythm / Q000145:classification; D003658:Decision Making, Computer-Assisted; D004569:Electroencephalography / Q000145:classification*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*; D012895:Sleep, REM / Q000502:physiology; D013826:Theta Rhythm / Q000145:classification