Effect of exercise intensity on the spectral properties of skin blood flow

Evaluating transient phenomena by wavelet analysis: early recovery to exercise

Wavelet analysis (WA) provides superior time-frequency decomposition of complex signals than conventional spectral analysis tools. To illustrate its usefulness in assessing transient phenomena, we applied a custom-developed WA algorithm to laser-Doppler (LD) signals of the cutaneous microcirculation measured at glabrous (finger pulp) and nonglabrous (forearm) sites during early recovery after dynamic exercise. This phase, importantly contributing to the establishment of thermal homeostasis after exercise cessation, has not been adequately explored because of its complex, transient form. Using WA, we decomposed the LD signals measured during the baseline and early recovery into power spectra of characteristic frequency intervals corresponding to endothelial nitric oxide (NO)-dependent, neurogenic, myogenic, respiratory, and cardiac physiological influence. Assessment of relative power (RP), defined as the ratio between the median power in the frequency interval and the median power of the total spectrum, revealed that endothelial NO-dependent (5.87 early recovery; 1.53 baseline; P = 0.005; Wilcoxon signed-rank test) and respiratory (0.71 early recovery; 0.40 baseline; P = 0.001) components were significantly increased, and myogenic component (1.35 early recovery; 1.83 baseline; P = 0.02) significantly decreased during early recovery in the finger pulp. In the forearm, only the RP of the endothelial NO-dependent (1.90 early recovery; 0.94 baseline; P = 0.009) component was significantly increased. WA presents an irreplaceable tool for the assessment of transient phenomena. The relative contribution of the physiological mechanisms controlling the microcirculatory response in the early recovery phase appears to differ in glabrous and nonglabrous skin when compared with baseline; moreover, the endothelial NO-dependent influence seems to play an important role.NEW & NOTEWORTHY We address the applicability of wavelet analysis (WA) in evaluating transient phenomena on a model of early recovery to exercise, which is the only exercise-associated phase characterized by a distinct transient shape and as such cannot be assessed using conventional tools. Our WA-based algorithm provided a reliable spectral decomposition of laser-Doppler (LD) signals in early recovery, enabling us to speculate roughly on the mechanisms involved in the regulation of skin microcirculation in this phase.

Detection of Site-Specific Blood Flow Variation in Humans during Running by a Wearable Laser Doppler Flowmeter

Wearable wireless physiological sensors are helpful for monitoring and maintaining human health. Blood flow contains abundant physiological information but it is hard to measure blood flow during exercise using conventional blood flowmeters because of their size, weight, and use of optic fibers. To resolve these disadvantages, we previously developed a micro integrated laser Doppler blood flowmeter using microelectromechanical systems technology. This micro blood flowmeter is wearable and capable of stable measurement signals even during movement. Therefore, we attempted to measure skin blood flow at the forehead, fingertip, and earlobe of seven young men while running as a pilot experiment to extend the utility of the micro blood flowmeter. We measured blood flow in each subject at velocities of 6, 8, and 10 km/h. We succeeded in obtaining stable measurements of blood flow, with few motion artifacts, using the micro blood flowmeter, and the pulse wave signal and motion artifacts were clearly separated by conducting frequency analysis. Furthermore, the results showed that the extent of the changes in blood flow depended on the intensity of exercise as well as previous work with an ergometer. Thus, we demonstrated the capability of this wearable blood flow sensor for measurement during exercise.

Modified thoracic impedance plethysmography to monitor sleep apnea syndromes

BACKGROUND AND PURPOSE

In order to identify sleep disorders by thoracic impedance plethysmography (TIP), we propose several new techniques: the application of an adaptive filter, a scaled Fourier linear combiner (SFLC) to eliminate cardiac-derived fluctuation in the impedance waveform, and the use of heart rate variability (HRV) to ascertain whether the airflow is obstructed.

PATIENTS AND METHODS

Laboratory simulation experiments on four healthy individuals and actual overnight measurements on five patients with sleep disorders were carried out. Amplified thoracic impedance change (DeltaZ), ECG, a phonocardiograph, a pneumotachograph and a standard polysomnograph were recorded. The SFLC was applied to DeltaZ to selectively extract the cardiac-synchronous component (DeltaZ(CSC)), and the remainder of the waveform (DeltaZ(REM)) was low-pass filtered to estimate the waveform driven by respiration. The HRV was divided into respiratory synchronous (HRV(R)) and low frequency (HRV(L)) components.

RESULTS

The SFLC could drastically extract DeltaZ(CSC) from DeltaZ and thereby demonstrate a DeltaZ(REM) pattern quite similar to the flow-volume curve of the pneumotachograph. Central sleep apnea could be identified as the cessation of DeltaZ(REM) and concomitant attenuation of HRV(R). Obstructive sleep apnea could be identified as the maintenance of rhythmic but attenuated variations of DeltaZ(REM) accompanied by asynchronous fluctuation of HRV(R) against DeltaZ(REM). Central hypopnea could be identified as a normal but attenuated waveform in both DeltaZ(REM) and HRV(R). A large fluctuation in HRV(L) was observed during repetitive appearances of apnea/hypopnea in the nocturnal experiments.

CONCLUSION

The modified TIP together with HRV provides a superior tool for accurate and convenient definition of sleep apnea syndromes.