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Background: Phase-rectified signal averaging (PRSA) was often applied to assess the cardiac vagal modulation. Despite its broad use, this method suffers from the confounding effects of anomalous variants of sinus rhythm. This study aimed to improve the original PRSA method in deceleration capacity (DC) quantification., Methods: The refined deceleration capacity (DC ref ) was calculated by excluding from non-vagally mediated abnormal variants of sinus rhythms. Holter recordings from 202 healthy subjects and 51 patients with end-stage renal disease (ESRD) have been used for validity. The DC ref was compared to original DC (DC org ) by the area under receiver operating characteristic curve., Results: Experimental results demonstrate that the original and refined DCs calculated from 24-h, 2-h, and 30-min Holter recordings are significantly lower in patients with ESRD than those in the healthy group. In receiver operating characteristic curve analysis, the DC ref provides better performance than the DC org in distinguishing between the patients with ESRD and healthy control subjects. Furthermore, the refined PRSA technique enhances the low frequency and attenuates high frequency components for spectral analysis in ESRD patients., Conclusions: The DC ref appears to reduce the influence of non-vagally mediated abnormal variants of sinus rhythm and highlighting the pathological influence. DC ref , especially assessed from short-term electrocardiography recordings, may be complementary to existing autonomic function assessment, risk stratification, and efficacy prediction strategies.

Rhythmic respiratory movement in a deep and slow pattern can be beneficial to cardiovascular system, this paper investigates the effect of step-wise paced breathing procedure on blood oxygen saturation (SpO2). Experiment objects were divided into two groups, the normobaric hypoxia (simulated altitude of 4 500 meters hypoxia environment, 8 persons), normoxia and normobaric group (laboratory environment, 49 persons). The respiratory movements were performed by a high-to-low progressive change in two groups respectively. During the experiment, each object's blood oxygen saturation and heart rate were recorded. Results showed that progressive guided breathing could significantly increase the subjects' blood oxygen saturation level from 90% to 95% under the hypoxic condition. Even under the normobaric and normoxic condition, progressive guided breathing with stable blood oxygen saturation level can also enhance the blood oxygen saturation level. In both groups, mean heart rate declined in the progressive guided breathing. The research showed that the step-wise paced breathing technique could regulate the blood oxygen saturation and effectively improve the level of blood oxygen saturation by adjusting the respiratory motion in the low oxygen environment.

Heart rate variability (HRV) analysis technology based on an autoregressive (AR) model is widely used in the assessment of autonomic nervous system function. The order of AR models has important influence on the accuracy of HRV analysis. This article presents a method to determine the optimum order of AR models. After acquiring the ECG signal of 46 healthy adults in their natural breathing state and extracting the beat-to-beat intervals (RRI) in the ECG, we used two criteria, i. e. final prediction error (FPE) criterion to estimate the optimum model order for AR models, and prediction error whiteness test to decide the reliability of the model. We compared the frequency domain parameters including total power, power in high frequency (HF), power in low frequency (LF), LF power in normalized units and ratio of LF/HF of our HRV analysis to the results of Kubios-HRV. The results showed that the correlation coefficients of the five parameters between our methods and Kubios-HRV were greater than 0.95, and the Bland-Altman plot of the parameters was in the consistent band. The results indicate that the optimization algorithm of HRV analysis based on AR models proposed in this paper can obtain accurate results, and the results of this algorithm has good coherence with those of the Kubios-HRV software in HRV analysis.

In comparison with the measurement of heart rate variability from ECG and from photoplethysmographic signal from 46 healthy adults in their spontaneous breathing state. The beat-to-beat intervals in ECG and pulse-to-pulse intervals in photoplethysmographic signal are extracted, and then the parameters of heart rate variability are calculated. Three kinds of algorithms are chosen to get the pulse-to-pulse intervals, which are the intervals of maximum of second derivative, the maximum of PPG signal and the tangent intersection. The results show that the correlation coefficients of the HRV parameters in the two calculation methods are highly correlated. The Bland-Altman scattered plots show the relative bias results from the algorithm of the maximum of PPG signal are smallest and singular points that deviate from the consistent limits are the least compared with the other two algorithms.

A design of handheld heart rate variability acquisition and analysis system is proposed. The system collects and stores the patient's ECG every five minutes through both hands touching on the electrodes, and then -uploads data to a PC through USB port. The system uses software written in LabVIEW to analyze heart rate variability parameters, The parameters calculated function is programmed and generated to components in Matlab.