Abstract
Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.
Highlights
Near infrared spectroscopy (NIRS) has been a low cost and effective technique for stimulus evoked function and activity research by non-invasively measuring the hemodynamic changes in specific brain regions, which can be classified to three different NIRS techniques including continuous wave NIRS (CW-NIRS), frequency domain NIRS (FD-NIRS) and time domain NIRS (TDNIRS), has attracted growing interest [1,2]
It should be underlined that the above mentioned method is proposed for CW-NIRS with multidistance probe configuration and could not be approved directly for TD-NIRS and FD-NIRS, which work in different ways
X(t)1⁄4 [x(t) x(t-1) ... x(t-M)]T, where x(t) is theD[HbO2] calculated from detector D1 based on the modified Lambert-Beer law (MLBL), which contains the physiological interference in superficial tissue and is used as the reference signal, M is the order of the adaptive filter and x(t-M) is the M-step delay signal of x(t)
Summary
Near infrared spectroscopy (NIRS) has been a low cost and effective technique for stimulus evoked function and activity research by non-invasively measuring the hemodynamic changes in specific brain regions, which can be classified to three different NIRS techniques including continuous wave NIRS (CW-NIRS), frequency domain NIRS (FD-NIRS) and time domain NIRS (TDNIRS), has attracted growing interest [1,2]. Compared with other functional brain activity measurement technologies such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), NIRS has its particular advantages such as safety, fewer physical restrictions, portability and greater practicality In addition to these advantages, NIRS is associated with a major problem that is the physiological interference, which mainly relates to perturbations caused by cardiac and respiratory events and is often sufficient to suppress the desired activation signal [3,4]. Zhang and Sun et al adopted the recursive least squares (RLS) method to improve the convergence rate and the performance of physiological interference suppression [7] Both methods are only effective to reduce the physiological interferences in the superficial head tissue layers, the physiological interferences in deeper tissue layers still obscure the desired haemodynamic changes of functional brain activation. It should be underlined that the above mentioned method is proposed for CW-NIRS with multidistance probe configuration and could not be approved directly for TD-NIRS and FD-NIRS, which work in different ways
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