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

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
Format: Artikel
Sprache:eng
Schlagworte:
Banks (Finance)
Biomedical and Life Sciences
Biomedicine
EEG
Electroencephalography
Filters
Neurophysiology
Neurosciences
Noise
Numerical analysis
Time series
Wavelet transforms
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Zusammenfassung: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
ISSN:0090-2977
1573-9007
DOI:10.1007/s11062-019-09783-y