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 |
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| creator | Kekovic, G Sekulic, S |
| 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 |
| doi_str_mv | 10.1007/s11062-019-09783-y |
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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. 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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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