Fig. 5

Overall design of the study. a Pre-processing steps taken to achieve the format of data as it is used in the neural networks. One of the 5 channels is first high-pass filtered with a cut-off at 0.2 Hz, then low-pass filtered with a cut-off at 49 Hz followed by a re-sampling to 100 Hz to ensure data homogeneity. In the case of EEG signals, a channel selection is employed to choose the channel with the least noise. The data are then encoded using either the CC or the octave encoding. b Steps taken to produce and test the automatic scoring algorithm. A part of the SSC^{10, 32} and WSC^{32, 33} is randomly selected, as described in Supplementary Table 1. These data are then segmented in 5 min segments and scrambled with segments from other subjects to increase batch similarity during training. A neural network is then trained until convergence (evaluated using a separate validation sample). Once trained, the networks are tested on a separate part of the SSC and WSC along with data from the IS-RC³¹ and KHC^{10, 34}. c Steps taken to produce and test the narcolepsy detector. Hypnodensities are extracted from data, as described in Supplementary Table 1. These data are separated into a training (60%) and a testing (40%) split. From the training split, 481 potentially relevant features, as described in Supplementary Table 9, are extracted from each hypnodensity. The prominent features are maintained using a recursive selection algorithm, and from these features a GP classifier is created. From the testing split, the same relevant features are extracted, and the GP classifier is evaluated