Detection of Change Points in Time Series with Moving Average Filters and Wavelet Transform: Application to EEG Signals

We investigated change point detection (CPD) in time series composed of harmonic functions driven by Gaussian noise (in EEGs, in particular) and proposed a method of moving average filters in conjunction with wavelet transform. Numerical simulations showed that CPD runs over 90% within the frequency...

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Veröffentlicht in:Neurophysiology (New York) 2019-01, Vol.51 (1), p.2-8
Hauptverfasser: Kekovic, G, Sekulic, S
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description We investigated change point detection (CPD) in time series composed of harmonic functions driven by Gaussian noise (in EEGs, in particular) and proposed a method of moving average filters in conjunction with wavelet transform. Numerical simulations showed that CPD runs over 90% within the frequency band
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subjects Banks (Finance)
Biomedical and Life Sciences
Biomedicine
EEG
Electroencephalography
Filters
Neurophysiology
Neurosciences
Noise
Numerical analysis
Time series
Wavelet transforms
title Detection of Change Points in Time Series with Moving Average Filters and Wavelet Transform: Application to EEG Signals
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