Abstract
Near-infrared spectroscopy (NIRS) can provide the hemodynamics information based on the hemoglobin concentration representing the blood oxygen metabolism of the cerebral cortical, which can be deployed for the cerebral function study. However, NIRS-based cerebral function detection accuracy can be significantly influenced by the physiological activities such as cardic cycle, respiration, spontaneous low-frequency oscillation and ultra-low frequency oscillation. The distribution difference of the capillary, artery and vein leads to the heterogeneity feature of the cerebral tissues. In the case that the heterogeneity is not serious, good detection accuracy and stable performance can be achieved through the regression analysis as the reference signal can well represent the interference in the measurement signal when conducting the multi-distance measurement approach. The direct use of the reference signal to estimate the interference is not able to achieve good performance in the case that the heterogeneity is serious. In this study, the cerebral function activity signal is extracted using recursive least square (RLS) method based on the multi-distance measurement method in which the reference signal is processed by ensemble empirical mode decomposition (EEMD) algorithm. The temporal and dimensional correlation of the neighboring sampling values are applied to estimate the interference in the measurement signal. Monte Carlo simulation based on a heterogeneous model is adopted here to investigate the effectiveness of this methodology. The results show that this methodology can effectively suppress the physiological interference and improve the detection accuracy of cerebral activity signal.
Highlights
Continuous-wave Near-Infrared spectroscopy (NIRS) which utilizes a constant-frequency or low-frequency modulated diode as the light source has been widely used in obtaining hemodynamic information of oxyhemoglobin, deoxyhemoglobin, etc.[1,2,3] The photodiode is often used as the detector
In order to capture the correlation information in physiological disturbance estimation and achieve better estimation results, we explore the method of ensemble empirical mode decomposition (EEMD) and bidimensional recursive least square (RLS) (EEMD-BRLS) by investigating and analyzing the tapped delay linelter
Targeting the issue of di®erent in°uences from various physiological activities on reference signal and synthetic signal caused by heterogeneity in the brain, the performances of RLS, EEMD-RLS and EEMD-BRLS algorithms are compared and analyzed using the mean squared errors (MSEs) from statistical results
Summary
Continuous-wave Near-Infrared spectroscopy (NIRS) which utilizes a constant-frequency or low-frequency modulated diode as the light source has been widely used in obtaining hemodynamic information of oxyhemoglobin, deoxyhemoglobin, etc.[1,2,3] The photodiode is often used as the detector. Compared to other cerebral function testing technologies such as electroencephalograph (EEG), magnetoencephalography (MEG), positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), continuous-wave NIRS has shown great capability in various cerebral activity signal detection with advantages of simple construction, lowcost and realized miniaturization.[4] the detection accuracy of NIRS cerebral function can be signicantly in°uenced by the physiological activities.[5]. Many international research groups have devoted a great deal of e®ort to studying physiological interference issues.[6,7] Physiological disturbances include cardiac cycle, respiration, spontaneous low frequency oscillation, and ultra-low frequency oscillation.[8] The sources of its e®ects are two-fold. The physiological interferences are referred to as global disturbances or systemic physiological disturbances
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