[Study on Indicator Densitometry Determination Method of Hemodynamic Parameters].

Measurement for hemodynamic parameters has always been a hot spot of clinical research. Methods for measuring hemodynamic parameters clinically have the problems of invasiveness, complex operation and being unfit for repeated measurement. To solve the problems, an indicator densitometry analysis method is presented based on near-infrared spectroscopy (NIRS) and indicator dilution theory, which realizes the hemodynamic parameters measured noninvasively. While the indocyanine green (ICG) was injected into human body, circulation carried the indicator mixing and diluting with the bloodstream. Then the near-nfrared probe was used to emit near-infrared light at 735, 805 and 940 nm wavelengths through the sufferer's fingertip and synchronously capture the transmission light containing the information of arterial pulse wave. By uploading the measured data, the computer would calculate the ICG concentration, establish continuous concentration curve and compute some intermediate variables such as the mean transmission time (MTT) and the initial blood ICG concentration (c(t0)). Accordingly Cardiac Output (CO) and Circulating Blood Volume (CBV) could be calculated. Compared with the clinical "gold standard" methods of thermodilution and I-131 isotope-labelling method to measure the two parameters by clinical controlled trials, ten sets of data were obtained. The maximum relative errors of this method were 8.88% and 4.28% respectively, and both of the average relative errors were below 5%. The result indicates that this method can meet the clinical accuracy requirement and can be used as a noninvasive, repeatable and applied solution for clinical hemodynamnic parameters measurement.

[EEMD-ICA Applied in Signal Extraction in Functional Near-Infrared Spectroscopy].

Currently, functional near-infrared spectroscopy (fNIRS) is widely used in the field of Neuroimaging. To solve the signal-noise frequency spectrum aliasing in non-linear and non-stationary fNIRS characteristic signal extraction, a new joint multi-resolution algorithm, EEMD-ICA, is proposed based on combining Independent Component Analysis with Ensemble Empirical Mode Decomposing. After functional brain imaging instrument detected the multi-channel and multi-wavelength NIR optical density signals, EEMD was performed to decompose measurement signals into multiple intrinsic mode function according to the signal frequency component. Then ICA was applied to extract the interest data from IMFs into ICs. Finally, reconstructed signals were obtained by accumulating the ICs set. EEMD-ICA was applied in de-noising Valsalva test signals which were considered as original signals and compared with Empirical Mode Decomposing and Ensemble Empirical Mode Decomposing to illustrate validity of this algorithm. It is proved that useful information loss during de-noising and invalidity of noise elimination are completely solved by EEMD-ICA. This algorithm is more optimized than other two de-noising methods in error parameters and signal-noise-ratio analysis.