Spectral Estimation of Random Signals

Spectral estimation of periodic and skew periodic random signals and approximation of spectral densities

Proceedings of the 33rd Chinese Control Conference ◽

10.1109/chicc.2014.6895847 ◽

2014 ◽

Cited By ~ 3

Author(s):

Axel Ringh ◽

Anders Lindquist

Keyword(s):

Spectral Estimation ◽

Spectral Densities ◽

Random Signals

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Random Signals and Spectral Estimation

Digital Signal Processing with Examples in MATLAB® ◽

10.1201/9781439837832-7 ◽

2016 ◽

pp. 199-230

Author(s):

Samuel D. Stearns ◽

Don R. Hush

Keyword(s):

Spectral Estimation ◽

Random Signals

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Statistical efficiency of correlation-based methods for ARMA spectral estimation

IEE Proceedings F Communications Radar and Signal Processing ◽

10.1049/ip-f-1.1983.0035 ◽

1983 ◽

Vol 130 (3) ◽

pp. 211 ◽

Cited By ~ 3

Author(s):

M. Kaveh ◽

S.P. Bruzzone

Keyword(s):

Spectral Estimation ◽

Statistical Efficiency

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Causal Probabilistic Network and Power Spectral Estimation Used in Sleep Stage Classification

Methods of Information in Medicine ◽

10.1055/s-0038-1636853 ◽

1997 ◽

Vol 36 (04/05) ◽

pp. 41-46

Author(s):

A. Kjaer ◽

W. Jensen ◽

T. Dyrby ◽

L. Andreasen ◽

J. Andersen ◽

...

Keyword(s):

Sleep Stage ◽

Spectral Estimation ◽

Interrater Agreement ◽

Probabilistic Networks ◽

Probabilistic Network ◽

Spectral Bands ◽

Power Spectral ◽

Stage Classification ◽

Sleep Stage Classification ◽

The Brain

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

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