Quantification of radial arterial pulse characteristics change during exercise and recovery

Advanced waveform analysis of the photoplethysmogram signal using complementary signal processing techniques for the extraction of biomarkers of cardiovascular function

Introduction

Photoplethysmogram signals from wearable devices typically measure heart rate and blood oxygen saturation, but contain a wealth of additional information about the cardiovascular system. In this study, we compared two signal-processing techniques: fiducial point analysis and Symmetric Projection Attractor Reconstruction, on their ability to extract new cardiovascular information from a photoplethysmogram signal. The aim was to identify fiducial point analysis and Symmetric Projection Attractor Reconstruction indices that could classify photoplethysmogram signals, according to age, sex and physical activity.

Methods

Three datasets were used: an in-silico dataset of simulated photoplethysmogram waves for healthy male participants (25-75 years old); an in-vivo dataset containing 10-min photoplethysmogram recordings from 57 healthy subjects at rest (18-39 or > 70 years old; 53% female); and an in-vivo dataset containing photoplethysmogram recordings collected for 4 weeks from a single subject, in daily life. The best-performing indices from the in-silico study (5/48 fiducial point analysis and 6/49 Symmetric Projection Attractor Reconstruction) were applied to the in-vivo datasets.

Results

Key fiducial point analysis and Symmetric Projection Attractor Reconstruction indices, which showed the greatest differences between groups, were found to be consistent across datasets. These indices were related to systolic augmentation, diastolic peak positioning and prominence, and waveform variability. Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques provided indices that supported the classification of age and physical activity, but not sex.

Conclusions

Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques demonstrated utility in identifying cardiovascular differences between individuals and within an individual over time. Future research should investigate the potential utility of these techniques for extracting information on fitness and disease, to support healthcare-decision making.

The Dicrotic Notch: Mechanisms, Characteristics, and Clinical Correlations

PURPOSE OF REVIEW

The dicrotic notch (DN) has long been considered a marker of arterial stiffness and compliance. Herein, we explored the recent developments in vascular medicine research in an attempt to assess the DN utility in clinical cardiovascular medicine.

RECENT FINDINGS

Since its discovery, several studies have attempted to measure the changes in different parameters of the DN in physiological and pathological states. Despite the significance of their findings, the clinical role of the DN remained limited. This may have been related to the difficulty of measuring the DN via indwelling arterial catheters in the past. However, over the past two decades, several non-invasive methods have been developed, which may re-ignite interest in DN research. The DN may have broader applications in clinical cardiovascular medicine. Further research is needed to establish the accuracy of DN non-invasive measurement methods and compare its prognostic value to other circulatory parameters.

Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy

Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aims to evaluate the application value of pulse waves in the diagnosis of hypertensive disorder in pregnancy.This research a retrospective study of pregnant women who attended prenatal care and labored at Beijing Haidian District Maternal and Child Health Hospital. We extracted maternal hemodynamic factors and measured the pulse wave of the pregnant women. We developed an HDP predictive model by using support vector machine algorithms at five-gestational-week stages.At five-gestational-week stages, the area under the receiver operating characteristic curve (AUC) of the predictive model with pulse wave parameters was higher than that of the predictive model with hemodynamic factors. The AUC values of the predictive model with pulse wave parameters were 0.77 (95% CI 0.64 to 0.9), 0.83 (95% CI 0.77 to 0.9), 0.85 (95% CI 0.81 to 0.9), 0.93 (95% CI 0.9 to 0.96) and 0.88 (95% CI 0.8 to 0.95) at five-gestational-week stages, respectively. Compared to the predictive models with hemodynamic factors, the predictive model with pulse wave parameters had better prediction effects on HDP.Pulse waves had good predictive effects for HDP and provided appropriate guidance and a basis for non-invasive detection of HDP.

Systematic Review and Regression Modeling of the Effects of Age, Body Size, and Exercise on Cardiovascular Parameters in Healthy Adults

PURPOSE

Blood pressure, cardiac output, and ventricular volumes correlate to various subject features such as age, body size, and exercise intensity. The purpose of this study is to quantify this correlation through regression modeling.

METHODS

We conducted a systematic review to compile reference data of healthy subjects for several cardiovascular parameters and subject features. Regression algorithms used these aggregate data to formulate predictive models for the outputs-systolic and diastolic blood pressure, ventricular volumes, cardiac output, and heart rate-against the features-age, height, weight, and exercise intensity. A simulation-based procedure generated data of virtual subjects to test whether these regression models built using aggregate data can perform well for subject-level predictions and to provide an estimate for the expected error. The blood pressure and heart rate models were also validated using real-world subject-level data.

RESULTS

The direction of trends between model outputs and the input subject features in our study agree with those in current literature.

CONCLUSION

Although other studies observe exponential predictor-output relations, the linear regression algorithms performed the best for the data in this study. The use of subject-level data and more predictors may provide regression models with higher fidelity.

SIGNIFICANCE

Models developed in this study can be useful to clinicians for personalized patient assessment and to researchers for tuning computational models.

Characterization of Rat Cardiovascular System by Anacrotic/Dicrotic Notches in the Condition of Increase/Decrease of NO Bioavailability

We characterized modes of action of NO-donor S-nitrosoglutathione (GSNO) and NO-synthase inhibitor l-NAME derived from dicrotic (DiN) and anacrotic (AnN) notches of rat arterial pulse waveform (APW) in the condition of increased/decreased NO bioavailability. The cross-relationship patterns of DiN and AnN with 34 hemodynamic parameters (HPs) induced by GSNO and l-NAME are presented. After GSNO bolus administration, approximate non-hysteresis relationships were observed in the difference between DiN-AnN (mmHg) blood pressure (BP) and other 19 HPs, suggesting that these HPs, i.e., their signaling pathways, responding to NO concentration, are directly connected. Hysteresis relationships were observed between DiN-AnN (mmHg) and other 14 HPs, suggesting that signaling pathways of these HPs are indirectly connected. The hysteresis relationships were only observed between the time interval DiN-AnN (ms) and other 34 HPs, indicating no direct connection of signaling pathways. The cross-relationship patterns of DiN-AnN (mmHg), but not DiN-AnN (ms), induced by l-NAME were in accordance to the increased NO bioavailability induced by GSNO. In conclusion, we found the non-hysteresis/hysteresis cross-relationship "patterns" of DiN-AnN intervals to other HPs in the presence of GSNO that revealed their direct or indirect signaling pathways connections. This may contribute to our understanding of biological effects of natural substances that modulate NO production and/or NO signaling pathways.

Pulse-Wave-Pattern Classification with a Convolutional Neural Network

Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in pattern classification due to extracting informative abstraction and features. For previous PWPC criteria, the relationship between pulse and disease types is not clear. In order to improve the clinical practicability, there is a need for a CNN model to find the one-to-one correspondence between pulse pattern and disease categories. In this study, five cardiovascular diseases (CVD) and complications were extracted from medical records as classification criteria to build pulse data set 1. Four physiological parameters closely related to the selected diseases were also extracted as classification criteria to build data set 2. An optimized CNN model with stronger feature extraction capability for pulse signals was proposed, which achieved PWPC with 95% accuracy in data set 1 and 89% accuracy in data set 2. It demonstrated that pulse waves are the result of multiple physiological parameters. There are limitations when using a single physiological parameter to characterise the overall pulse pattern. The proposed CNN model can achieve high accuracy of PWPC while using CVD and complication categories as classification criteria.

Measuring Vascular Recovery Rate After Exercise †

The rate at which an individual recovers from exercise is known to be indicative of cardiovascular risk. It has been widely shown that the reduction in heart rate immediately after exercise is predictive of mortality. However, little research has been conducted into whether the time taken for the blood vessels to return to normal is also indicative of risk. In this study, we present a novel approach to assess vascular recovery rate (VRR) using the photoplethysmogram (PPG) signal, which is monitored by smart wearables. The VORTAL dataset (http://peterhcharlton.github.io/RRest/) was used for this study, containing PPG signals from 39 healthy subjects before (baseline) and after exercise. 31 VRR indices were extracted from the PPG pulse wave shape, as well as heart rate for comparison. The rate at which indices returned to baseline after exercise was quantified, and the consistency of changes between subjects was assessed statistically. Many VRR indices exhibited changes after exercise which were consistent between subjects. Indices derived from the timings and second derivative of pulse waves were identified as candidates for future work. The rate at which the indices returned to baseline differed between indices and subjects, indicating that they may provide additional information beyond that of heart rate, and that they may be useful for stratifying subjects. This study demonstrated the feasibility of assessing VRR after exercise from the PPG. Future studies should investigate whether VRR indices are associated with cardiovascular fitness, and the potential utility of incorporating the indices into wearable sensors.

Changes of Arterial Pulse Waveform Characteristics with Gestational Age during Normal Pregnancy

Arterial pulse waveform analysis has been widely used to reflect physiological changes in the cardiovascular system. This study aimed to comprehensively investigate the changes of waveform characteristics of both photoplethysmographic (PPG) and radial pulses with gestational age during normal pregnancy. PPG and radial pulses were simultaneously recorded from 130 healthy pregnant women at seven gestational time points. After normalizing the arterial pulse waveforms, the abscissa of notch point, the total pulse area and the reflection index were extracted and compared between different measurement points and between the PPG and radial pulses using post-hoc multiple comparisons with Bonferrioni correction. The results showed that the effect of gestational age on all the three waveform characteristics was significant (all p < 0.001) after adjusting for maternal age, heart rate and blood pressures. All the three waveform characteristics demonstrated similar changing trends with gestational age, and they were all significantly different between the measurements from gestational week 12–15 and the others (all p < 0.05, except for the PPG total pulse area between the first and second measurement points). In conclusion, this study has comprehensively quantified similar changes of both PPG and radial pulse waveform characteristics with gestational age.