title: Optimized echo state networks with leaky integrator neurons for EEG-based microsleep detection.
abstract: The performance of a microsleep detection system was calculated in terms of its ability to detect the behavioural microsleep state (1-s epochs) from spectral features derived from 16-channel EEG sampled at 256 Hz. Best performance from a single classifier model was achieved using leaky integrator neurons on an echo state network (ESN) classifier with a mean phi correlation (φ) of 0.38 and accuracy of 67.3%. A single classifier model of ESN with sigmoidal inputs achieved φ of 0.20 and accuracy of 48.5% and a single classifier model of linear discriminant analysis (LDA) achieved φ of 0.31 and accuracy of 53.6%. However, combining the output of several single classifier models (ensemble learning) via stacked generalization of the ESN with leaky integrator neurons approach led to a substantial increase in detection performance of φ of 0.51 and accuracy of 81.2%. This is a substantial improvement of our previous best result of φ = 0.39 on this data with LDA and stacked generalization.
authors.forename: Sudhanshu S D P
authors.initials: SS
authors.lastname: Ayyagari
authors.forename: Richard D
authors.initials: RD
authors.lastname: Jones
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Weddell
mesh_terms: D000293:Adolescent; D000328:Adult; D016002:Discriminant Analysis; D004569:Electroencephalography; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology*; D055815:Young Adult