A new technique for time series analysis, which is a combination of the maximum entropy method (MEM) for spectral analysis and the non-linear least squares method (LSM) for fitting analysis, is described. In this technique, the MEM power spectral density (MEMPSD) is calculated using a very large lag that could diminish the lag dependence of dominant periods estimated by the MEM analysis. The validity of this large lag is confirmed by the LSM, given that the ten dominant MEM periods are known quantities. To validate the MEM plus LSM technique, it is compared with autoregressive (AR) modelling, by analysing heart rate variability under pharmacological interventions (phenylephrine and trinitroglycerine), using 16 young males. The results indicate that the MEMPSD, when compared with the ARPSD, has numerous periods that could reproduce the original time series much more accurately, as revealed by the LSM analysis. However, both the low- and high-frequency powers with MEMPSD and ARPSDs shift in the expected directions in accordance with the pharmacological effects on the cardiovascular system. The implications of these results are discussed from the theoretical and practical standpoints of the MEM plus LSM technique, compared with AR modelling.

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Near-infrared finger photoplethysmograms were recorded and double-normalized pulse volumes (DNPV = DeltaV(b)/V(b); V(b) = total blood volume in the fingertip, DeltaV(b) = pulsatile component of V(b)) were calculated in ten subjects during, immersion of the contralateral hand in water at three different temperatures (44 degrees C, 22 degrees C, 11 degrees C). The DNPV from the left finger was compared beat-by-beat with cutaneous vascular resistance (CVR) derived by dividing mean blood pressure of the left third finger by cutaneous blood flow of the left fourth finger. The correlations overall at the three temperatures between log DNPV(LF) and log CVR(LF) (LF, low frequency component of DNPV and CVR) ranged from -0.89 to -0.96 among the subjects. After adjusting for a maximal extension of the vascular wall (DNPV(max)), the correlations became stronger. It was concluded that DNPV was a reliable and valid indicator of vascular tone in the finger.

Stiffening of the small artery may be the earliest sign of arteriosclerosis. However, there is no adequate method for directly assessing small arterial stiffness. In this study, the finger arterial elasticity index (FEI) was defined as the parameter n which denotes the curvilinearity of an exponential model of pressure (P)-volume (V(a)) relationship (V(a) = a - b exp (-nP)). For the original estimation, the FEI was calculated from a compliance index from the finger photoplethysmogram whilst occluding the finger. A simple estimation of the FEI was devised by utilizing normalized pulse volume instead of the compliance index. Both estimations yielded close agreement with the exponential model in healthy young participants (study 1: n = 19). Since the FEI was dependent on finger mean blood pressure, normalized finger arterial stiffness index (FSI) was defined as standardized residual from their relationship: mean and standard deviation (SD) of the FSI were 50 ± 10 (study 2: n = 174). The mean coefficient of variation of the FSI for four measurements was 5.72% (study 3: n = 6). The mean and SD of the FSI in seven arteriosclerotic patients were 100.0 ± 13.5. In conclusion, the FEI and FSI by simple estimation are valid and useful for arteriosclerosis research.