Dynamic phasor estimates through maximally flat differentiators

Platas-Garza, Miguel Angel; Serna, José Antonio de la O

doi:10.1109/pes.2008.4596206

Cited by 3 publications

(4 citation statements)

References 18 publications

(17 reference statements)

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“…where W is a weighting matrix, whose diagonal contains the samples of a Kaiser window (α = 10) [19] and B = [ψ (k,h) ] with h ∈ S and k = 0, · · · K. TFT provides a signal approximation…”

Section: Dynamic Physiological Noise Templatementioning

confidence: 99%

“…In the first stage, a super-resolution method, based on compressive sensing (CS) theory, provides accurate Fourier coefficients from a short sequence of fNIRS samples [18]. In the second stage, detected frequencies are used to form a basis in Taylor-Fourier transform (TFT) space [19]. The noise template is defined by projecting the sampled signal over the subspace spanned by this basis.…”

Section: Introductionmentioning

confidence: 99%

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A compressive sensing spectral model for fNIRS haemodynamic response de-noising

Frigo

Brigadoi

Giorgi

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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In the biomedical scenario, near-infrared spectroscopy (NIRS) is employed as a non-invasive brain imaging technique. In particular, functional near-infrared spectroscopy (fNIRS) measures the brain response, also known as haemodynamic response (HR), to pre-defined stimuli. Processing of fNIRS data requires a great effort to extrapolate the informative component from a noisy mixture of physiological and spurious contributions.In this paper a novel fNIRS de-noising algorithm is presented and validated over both synthetic ideal and synthetic realistic data. The short-separation channel signal is divided into nonoverlapping short sequences. For each of them, a specific noise model is identified and subtracted from the corresponding standard channel data. The algorithm relies on a combination of a super-resolution technique based on Compressive Sensing theory and spectral analysis performed via Taylor-Fourier transform.Preliminary experimental results show a significant reduction of spurious components in all the considered conditions. No significant distortions are introduced in the recovered HR, ensuring reliable clinical interpretation of the acquired trace.This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.978-1-4799-6477-2/15/$31.00 ©2015 IEEE

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Section: Dynamic Physiological Noise Templatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A compressive sensing spectral model for fNIRS haemodynamic response de-noising

Frigo

Brigadoi

Giorgi

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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“…where W is a weighting matrix, whose diagonal contains the samples of a Kaiser window (α = 10) [4] and B = [ψ (k,h) ] with h ∈ S and k = 0, · · · K.…”

Section: B Taylor-fourier Analysismentioning

confidence: 99%

“…For this purpose, a super-resolution method, based on compressive sensing (CS) theory, provides accurate Fourier coefficients from a short set of EEG samples [3]. In the second stage, detected frequencies are used to form a basis in Taylor-Fourier transform (TFT) space [4]. The template for GRA removal is defined by projecting the sampled signal over the subspace spanned by this basis.…”

Section: Introductionmentioning

confidence: 99%

EEG gradient artifact removal by compressive sensing and Taylor-Fourier transform

Frigo

Narduzzi

2014

2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represents a powerful tool for brain activity investigation. Unfortunately, EEG data collected during concurrent fMRI are affected by very large artifacts. This paper focuses on the gradient artifact (GRA), related to the sawtooth profiles of magnetic flux inside the MRI scanner. A novel removal algorithm is proposed and validated on both simulation and experimental data. A super-resolution method, based on compressive sensing, determines GRA harmonic frequencies.Amplitudes and phases of GRA components are estimated by means of the Taylor-Fourier transform (TFT), complying with dynamic operating conditions. Unlike averaging techniques, wellknown in the literature, this allows computation of a specific template for each artifact occurrence, which is subtracted from the original data.Experimental results show a significant reduction of spurious components in all the considered conditions. No significant distortions are introduced in spectral power distribution, allowing reliable clinical interpretation of the acquired trace.

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