Detection of Change Points in Time Series with Moving Average Filters and Wavelet Transform: Application to EEG Signals
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
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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 <40 Hz. This means that detection of structural change points is almost guaranteed in the respective cases. The mean absolute error (MAE) as a measure of performance of the method was below 5%. The method is rather robust against noise. It has been demonstrated that CPD is possible at the noise amplitude exceeding 25% of the amplitude of harmonic functions. In application of the proposed method on the signals, CPD appeared in 74% of the analyzed EEGs.
Keywords:Change point detection (CPD); Filter bank; Wavelet transform; Moving avarage filters; Gaussian noise; Frequency; EEG
Source:Neurophysiology, 2019, 51, 1, 2-8
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