Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MeMeA.2015.7145207 | Medical Measurements and Applications |
Keywords | Field | DocType |
Fourier transforms,brain,compressed sensing,haemodynamics,infrared spectroscopy,medical signal processing,signal denoising,spectral analysis,Compressive Sensing theory,HR,Taylor-Fourier transform,brain response,compressive sensing spectral model,fNIRS denoising algorithm,fNIRS haemodynamic response denoising,functional near-infrared spectroscopy,informative component,noisy mixture,noninvasive brain imaging technique,nonoverlapping short sequences,physiological contribution,predefined stimuli,short-separation channel signal,specific noise model,spectral analysis,spurious contribution,standard channel data,super-resolution technique,synthetic ideal data,synthetic realistic data,Taylor-Fourier transform,compressive sensing,de-noising,functional near-infrared spectroscopy,super-resolution | Biomedical engineering,Haemodynamic response,Speech recognition,Neuroimaging,Spectroscopy,Spurious relationship,Materials science,Compressed sensing | Conference |
Citations | PageRank | References |
2 | 0.38 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| guglielmo frigo | 1 | 55 | 10.64 |
| Brigadoi, S. | 2 | 2 | 0.38 |
| Giorgi, G. | 3 | 2 | 0.38 |
| Sparacino, G. | 4 | 2 | 0.38 |