Autoassociative MLP in Sleep Spindle Detection

Authors:
E. Huupponen;A. Värri;S-L. Himanen;J. Hasan;M. Lehtokangas;J. Saarinen
Affiliations:
Signal Processing Laboratory, Tampere University of Technology;Signal Processing Laboratory, Tampere University of Technology;Department of Clinical Neurophysiology, Tampere University Hospital;Department of Clinical Neurophysiology, Tampere University Hospital;Signal Processing Laboratory, Tampere University of Technology;Signal Processing Laboratory, Tampere University of Technology
Venue:
Journal of Medical Systems
Year:
2000

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Abstract

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.