To investigate the effect of stepwise paced breathing (PB) on pulse transit time (PTT), we collected physiological signals of electrocardiogram (ECG), respiration and arterial pulse wave during a procedure of stepwise PB, which consists of 6 different breathing rates changing in a protocol of 14.0-12.5-11.0-9.5-8.0-7.0 breath per minute (BPM), with each breathing rate lasting 3 minutes. Twenty two healthy adults involved in this experiment and the change of PTT was analyzed during the stepwise PB procedure. In our study, the PTT was measured by calculating the time interval from the R-spike of the ECG to the peaks of the second derivative of the arterial pulse wave. Ensemble empirical mode decomposition (EEMD) was applied to PTT to decompose the signal into different intrinsic mode function, and respiratory oscillation and trend component (baseline) in PTT were further extracted. It was found that the respiratory oscillations in the PTT increased with decreasing of the PB rate, and many of the subjects (14 out of 22) showed the phenomena of PTT baseline increasing during the stepwise PB procedure. The results indicated that the stepwise PB procedure induced a high level of cardiovascular oscillation and produced an accumulative effect of PTT baseline increase. As PTT is capable of predicting changes in BP over a short period of time, increase of PTT baseline indicates the decrease of blood pressure. The experiments showed that the stepwise PB procedure could reduce blood pressure for most subjects. For future work, it is necessary to develop certain indices differentiating the effectiveness of the stepwise PB procedure on the PTT baseline change, and to test the effectiveness of this stepwise PB procedure on blood pressure reduction for patients with essential hypertension.
Aiming at the defects that the traditional pulse transit time (PTT) detection methods are sensitive to changes in photoplethysmography (PPG) signal and require heavy computation, we proposed a new algorithm to detect PTT based on waveform time domain feature and dynamic difference threshold. We calculated the PTT by using dynamic difference threshold method to detect the R-waves of electrocardiogram (ECG), shortening the main peak detection range in PPG signal according to the characteristics of the waveform time domain, and using R wave to detect the main peak of PPG signal. We used the American MIMIC database and laboratory test data to validate the algorithm. The experimental results showed that the proposed method could accurately extract the feature points and detect PTT, and the PTT detection accuracies of the measurements and the database samples were 99.1% and 97.5%, respectively. So the proposed method could be better than the traditional methods.