Abstract
Near infrared spectroscopy (NIRS) is a non-invasive technique having the potential to allow the study of brain functional activation through the monitoring of changes in haemodynamic variables. However, the NIRS data derived in this application are often contaminated by physiological interference arising, for example, from cardiac contraction, breathing, and spontaneous low frequency oscillations. There have therefore been efforts to develop signal processing schemes aimed at improving signal quality. In the present paper, the multidistance NIRS probe configuration has been adopted. The near-distance source-detector pair is used to derive the superficial haemodynamic changes and the fardistance source-detector pair is used to derive deep haemodynamic changes. The recursive least squares algorithm was used to process the NIRS data in order to suppress physiological interference. We also utilized Monte Carlo simulations, based on a five-layer model of the adult human head, to evaluate our methodology. The results suggest that the recursive least squares approach has the potential to reduce physiological interference in NIRS data. Important advantages of this method are that it is adaptive and it can be used for real-time signal extraction of brain activity.
| Original language | English |
|---|---|
| Title of host publication | IFMBE Proceedings |
| Pages | 371-374 |
| Number of pages | 4 |
| Volume | 39 IFMBE |
| DOIs | |
| Publication status | Published - 2013 |
| Event | World Congress on Medical Physics and Biomedical Engineering - Beijing, China Duration: May 26 2012 → May 31 2012 |
Other
| Other | World Congress on Medical Physics and Biomedical Engineering |
|---|---|
| Country | China |
| City | Beijing |
| Period | 5/26/12 → 5/31/12 |
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Keywords
- Lambert-Beer law
- Monte Carlo
- Multidistance measurement
- Near infrared spectroscopy
- Recursive least squares
ASJC Scopus subject areas
- Biomedical Engineering
- Bioengineering
Cite this
Signal extraction of brain activity response by near infrared spectroscopy based on recursive least squares adaptive filtering. / Zhang, Y.; Sun, J. W.; Yang, C. L.; Zhu, M.; Zhang, B.; Scopesi, F.; Serra, G.; Rolfe, P.
IFMBE Proceedings. Vol. 39 IFMBE 2013. p. 371-374.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Signal extraction of brain activity response by near infrared spectroscopy based on recursive least squares adaptive filtering
AU - Zhang, Y.
AU - Sun, J. W.
AU - Yang, C. L.
AU - Zhu, M.
AU - Zhang, B.
AU - Scopesi, F.
AU - Serra, G.
AU - Rolfe, P.
PY - 2013
Y1 - 2013
N2 - Near infrared spectroscopy (NIRS) is a non-invasive technique having the potential to allow the study of brain functional activation through the monitoring of changes in haemodynamic variables. However, the NIRS data derived in this application are often contaminated by physiological interference arising, for example, from cardiac contraction, breathing, and spontaneous low frequency oscillations. There have therefore been efforts to develop signal processing schemes aimed at improving signal quality. In the present paper, the multidistance NIRS probe configuration has been adopted. The near-distance source-detector pair is used to derive the superficial haemodynamic changes and the fardistance source-detector pair is used to derive deep haemodynamic changes. The recursive least squares algorithm was used to process the NIRS data in order to suppress physiological interference. We also utilized Monte Carlo simulations, based on a five-layer model of the adult human head, to evaluate our methodology. The results suggest that the recursive least squares approach has the potential to reduce physiological interference in NIRS data. Important advantages of this method are that it is adaptive and it can be used for real-time signal extraction of brain activity.
AB - Near infrared spectroscopy (NIRS) is a non-invasive technique having the potential to allow the study of brain functional activation through the monitoring of changes in haemodynamic variables. However, the NIRS data derived in this application are often contaminated by physiological interference arising, for example, from cardiac contraction, breathing, and spontaneous low frequency oscillations. There have therefore been efforts to develop signal processing schemes aimed at improving signal quality. In the present paper, the multidistance NIRS probe configuration has been adopted. The near-distance source-detector pair is used to derive the superficial haemodynamic changes and the fardistance source-detector pair is used to derive deep haemodynamic changes. The recursive least squares algorithm was used to process the NIRS data in order to suppress physiological interference. We also utilized Monte Carlo simulations, based on a five-layer model of the adult human head, to evaluate our methodology. The results suggest that the recursive least squares approach has the potential to reduce physiological interference in NIRS data. Important advantages of this method are that it is adaptive and it can be used for real-time signal extraction of brain activity.
KW - Lambert-Beer law
KW - Monte Carlo
KW - Multidistance measurement
KW - Near infrared spectroscopy
KW - Recursive least squares
UR - http://www.scopus.com/inward/record.url?scp=84876034967&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876034967&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-29305-4_99
DO - 10.1007/978-3-642-29305-4_99
M3 - Conference contribution
AN - SCOPUS:84876034967
SN - 9783642293047
VL - 39 IFMBE
SP - 371
EP - 374
BT - IFMBE Proceedings
ER -