Name
Abstract | ||
|---|---|---|
A new algorithm for the detection of oscillatory events in the EEG is presented. By estimating autoregressive models on short segments the sleep EEG is described as a superposition of stochastically driven harmonic oscillators with damping and frequencies varying in time. Oscillatory events are detected, whenever the damping of one or more frequencies is smaller than a predefined threshold. The algorithm works well for the detection of sleep spindles as well as for delta and alpha waves. The distribution of the time intervals between the detected sleep spindles shows maxima around 3–4s. It is discussed whether this maximum originates from slow oscillations or from stochasticity. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.neucom.2004.01.033 | Neurocomputing |
Keywords | Field | DocType |
Human sleep EEG,AR-model,Sleep spindles,Slow oscillations | Autoregressive model,Oscillation,Sleep spindle,Superposition principle,Pattern recognition,Mathematical analysis,Artificial intelligence,Maxima,Mathematics,Alpha wave,Harmonic oscillator,Electroencephalography | Journal |
Volume | ISSN | Citations |
58 | 0925-2312 | 5 |
PageRank | References | Authors |
1.49 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eckehard Olbrich | 1 | 135 | 16.51 |
| Peter Achermann | 2 | 31 | 7.06 |