Name
Abstract | ||
|---|---|---|
In this paper, we shall introduce an algorithm that classifies EEG data into five sleep stages, relying only on two-channel sleep measurements. The sleep of a patient (divided into intervals of 30 seconds) is assumed to be a Markov chain on the five-element state space of sleep stages and our aim is to compute the most probable chain of this hidden Markov model by a maximum a posteriori (MAP) estimation in the Bayesian framework. Both the prior distribution of the chains and the likelihood model have to be trained on manual classifications made by professionals. For this purpose, the data is first preprocessed by a Fourier transform, a log transform and a principal component analysis for dimensionality reduction. Since the number of possible chains is immense (roughly 10335), a heuristic approach for the computation of the MAP estimator is introduced, that systematically discards unlikely chains. The sleep stage classification is then compared to the classification of a professional, who scores according to the AASM and uses a full polysomnography. The overall structure of the hypnogram can adequately be reconstructed with error rates around 25%. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5220/0006242802360241 | PROCEEDINGS OF THE 10TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 4: BIOSIGNALS |
Keywords | Field | DocType |
Automatic Sleep Staging, Two-channel Measurement, Bayesian Statistics, Hidden Markov Model, MAP | Heuristic,Pattern recognition,Computer science,Communication channel,Artificial intelligence,Machine learning,Estimator | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shirin Riazy | 1 | 0 | 0.34 |
| Tilo Wendler | 2 | 0 | 0.34 |
| Jürgen Pilz | 3 | 50 | 12.09 |
| Martin Glos | 4 | 17 | 4.76 |
| Thomas Penzel | 5 | 76 | 13.73 |