Effective extraction and filtering of frequency components in physiological signals using sum-of-sinusoids modelling

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In biological signal processing, modelling and extraction of specific frequency components constitute an important procedure for filtering signal components of interest as well as artefact removal. Under some interference scenarios, a satisfactory elimination of artefacts from the signal must be even performed by subtraction of an artefact waveform model or template, rather than the use of linear band-pass filters. That is the case of the gradient artefact induced in the EEG within the fMRI scanner, which cannot be characterized by a specific bandwidth or spectral content. This paper presents a simple and accurate approach based upon sum-of-sinusoids modelling for signal and artefact frequency components representation in physiological signals. According to the proposed method, each signal frequency component is approximated as a sinusoid, whose amplitude and phase parameters are estimated by making use of the Discrete Fourier Transform (DFT). The proposed approach reveals to perform an effective modelling and extraction of ECG signal components as well as underlying gradient artefacts in the EEG signal.
Originele taal-2Engels
Pagina's (van-tot)73-80
Aantal pagina's8
TijdschriftSignal Processing Research
Volume2
Nummer van het tijdschrift4
StatusGepubliceerd - 2013

Vingerafdruk

Electroencephalography
Bandpass filters
Electrocardiography
Discrete Fourier transforms
Signal processing
Bandwidth
Magnetic Resonance Imaging

Citeer dit

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abstract = "In biological signal processing, modelling and extraction of specific frequency components constitute an important procedure for filtering signal components of interest as well as artefact removal. Under some interference scenarios, a satisfactory elimination of artefacts from the signal must be even performed by subtraction of an artefact waveform model or template, rather than the use of linear band-pass filters. That is the case of the gradient artefact induced in the EEG within the fMRI scanner, which cannot be characterized by a specific bandwidth or spectral content. This paper presents a simple and accurate approach based upon sum-of-sinusoids modelling for signal and artefact frequency components representation in physiological signals. According to the proposed method, each signal frequency component is approximated as a sinusoid, whose amplitude and phase parameters are estimated by making use of the Discrete Fourier Transform (DFT). The proposed approach reveals to perform an effective modelling and extraction of ECG signal components as well as underlying gradient artefacts in the EEG signal.",
author = "J.L. Ferreira and R.M. Aarts and P.J.M. Cluitmans",
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Effective extraction and filtering of frequency components in physiological signals using sum-of-sinusoids modelling. / Ferreira, J.L.; Aarts, R.M.; Cluitmans, P.J.M.

In: Signal Processing Research, Vol. 2, Nr. 4, 2013, blz. 73-80.

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

TY - JOUR

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AB - In biological signal processing, modelling and extraction of specific frequency components constitute an important procedure for filtering signal components of interest as well as artefact removal. Under some interference scenarios, a satisfactory elimination of artefacts from the signal must be even performed by subtraction of an artefact waveform model or template, rather than the use of linear band-pass filters. That is the case of the gradient artefact induced in the EEG within the fMRI scanner, which cannot be characterized by a specific bandwidth or spectral content. This paper presents a simple and accurate approach based upon sum-of-sinusoids modelling for signal and artefact frequency components representation in physiological signals. According to the proposed method, each signal frequency component is approximated as a sinusoid, whose amplitude and phase parameters are estimated by making use of the Discrete Fourier Transform (DFT). The proposed approach reveals to perform an effective modelling and extraction of ECG signal components as well as underlying gradient artefacts in the EEG signal.

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