Architecture of sleep: automated sleep stage analysis based on spatial EEG patterns

Introduction:

Alternatively to analyzing EEG frequency bands, sleep stages can also be separated using momentary signal amplitudes of the different recording channels. Based on this finding, we present a real time application that estimates the sleep stages by using measured amplitude vectors.

Methods:

The sleep stage specific distributions of amplitude vectors correspond to clusters in a 3-dimensional space. From these clusters we derive continuous probability distributions by summing multivariate Gaussian distributions. With Bayesian statistics we compute the current probabilities for all sleep stages from these probability distributions of measured data. The more amplitude vectors are measured the more exact the classification becomes, even for highly overlapping clusters of different sleep stages. Sudden changes of sleep stage can be detected by multiplication with a Markov matrix.

Results:

In order to validate the method we measured three EEG channels (F4, C4, O2) from 40 subjects. A part of the 3-dimensional amplitude vectors served as training data set for the algorithm. Subsequently, all amplitude vectors were used as test data set. A comparison with manual classification resulted in an accuracy of more than 90%.

Conclusions:

In principle, using our method we can estimate the momentary sleep stages fully automated, in real time and with high accuracy. In future, the performance of our method could be further increased by recording from more than three EEG channels.

No conflict of interest has been declared by the author(s).

Dr. med. Maximilian Traxdorf

Hals-Nasen-Ohren-Klinik, Kopf und Halschirurgie,

Universitätsklinikum Erlangen, Waldstr.191054,

Erlangen

Email: [e-mail]

Publication History

Publication Date:
18 April 2018 (online)