Introduction:
Heart Rate Variability (HRV) and Pulse Rate Variability (PRV), are non-invasive techniques for monitoring changes in the cardiac cycle. Both techniques have been used for assessing the ...autonomic activity. Although highly correlated in healthy subjects, differences in HRV and PRV have been observed under various physiological conditions. The reasons for their disparities in assessing the degree of autonomic activity remains unknown.
Methods:
To investigate the differences between HRV and PRV, a whole-body cold exposure (CE) study was conducted on 20 healthy volunteers (11 male and 9 female, 30.3 ± 10.4 years old), where PRV indices were measured from red photoplethysmography signals acquired from central (ear canal, ear lobe) and peripheral sites (finger and toe), and HRV indices from the ECG signal. PRV and HRV indices were used to assess the effects of CE upon the autonomic control in peripheral and core vasculature, and on the relationship between HRV and PRV. The hypotheses underlying the experiment were that PRV from central vasculature is less affected by CE than PRV from the peripheries, and that PRV from peripheral and central vasculature differ with HRV to a different extent, especially during CE.
Results:
Most of the PRV time-domain and Poincaré plot indices increased during cold exposure. Frequency-domain parameters also showed differences except for relative-power frequency-domain parameters, which remained unchanged. HRV-derived parameters showed a similar behavior but were less affected than PRV. When PRV and HRV parameters were compared, time-domain, absolute-power frequency-domain, and non-linear indices showed differences among stages from most of the locations. Bland-Altman analysis showed that the relationship between HRV and PRV was affected by CE, and that it recovered faster in the core vasculature after CE.
Conclusion:
PRV responds to cold exposure differently to HRV, especially in peripheral sites such as the finger and the toe, and may have different information not available in HRV due to its non-localized nature. Hence, multi-site PRV shows promise for assessing the autonomic activity on different body locations and under different circumstances, which could allow for further understanding of the localized responses of the autonomic nervous system.
Heart rate variability has been largely used for the assessment of cardiac autonomic activity, due to the direct relationship between cardiac rhythm and the activity of the sympathetic and ...parasympathetic nervous system. In recent years, another technique, pulse rate variability, has been used for assessing heart rate variability information from pulse wave signals, especially from photoplethysmography, a non-invasive, non-intrusive, optical technique that measures the blood volume in tissue. The relationship, however, between pulse rate variability and heart rate variability is not entirely understood, and the effects of cardiovascular changes in pulse rate variability have not been thoroughly elucidated. In this review, a comprehensive summary of the applications in which pulse rate variability has been used, with a special focus on cardiovascular health, and of the studies that have compared heart rate variability and pulse rate variability is presented. It was found that the relationship between heart rate variability and pulse rate variability is not entirely understood yet, and that pulse rate variability might be influenced not only due to technical aspects but also by physiological factors that might affect the measurements obtained from pulse-to-pulse time series extracted from pulse waves. Hence, pulse rate variability must not be considered as a valid surrogate of heart rate variability in all scenarios, and care must be taken when using pulse rate variability instead of heart rate variability. Specifically, the way pulse rate variability is affected by cardiovascular changes does not necessarily reflect the same information as heart rate variability, and might contain further valuable information. More research regarding the relationship between cardiovascular changes and pulse rate variability should be performed to evaluate if pulse rate variability might be useful for the assessment of not only cardiac autonomic activity but also for the analysis of mechanical and vascular autonomic responses to these changes.
•Fast permutation entropy is proposed for online analysis of PRV signals.•The feasibility of developing low-cost wearable devices to verify the proposed method.•The computational speed of the ...proposed method is demonstrated by simulated and measured data to be faster than the traditional permutation entropy.
Pulse rate variability (PRV) signals are extracted from pulsation signal can be effectively used for cardiovascular disease monitoring in wearable devices. Permutation entropy (PE) algorithm is an effective index for the analysis of PRV signals. However, PE is computationally intensive and impractical for online PRV processing on wearable devices. Therefore, to overcome this challenge, a fast permutation entropy (FPE) algorithm is proposed based on the microprocessor data updating process in this paper, which can analyze PRV signals with single-sample recursive. The simulation data and PRV signals extracted from pulse signals in “Fantasia database” were utilized to verify the performance and accuracy of the improved methods. The results show that the speed of FPE is 211 times faster than PE and maintain the accuracy of algorithm (Root Mean Squared Error = 0) for simulation data with a length of 10,000 samples and embedded dimension m = 5, time delay τ = 5, buffer length Lw = 512. For the RRV signals with 3000∼5000 samples, the result show that the consumption of FPE is less than 0.2 s, which is 175 times faster than PE. This indicates that FPE has better application performance than PE. Furthermore, a low-cost wearable signal detection system is developed to verify the proposed method, the result show that the proposed method can calculate the FPE of PRV signal online with single-sample recursive calculation. Subsequently, entropy-based features are used to explore the performance of decision trees in identifying life-threatening arrhythmias, and the method resulted in a classification accuracy of 85.43%. It can therefore be inferred that the proposed method has great potential in cardiovascular disease.
•A novel sequence coding-based 1D-GLCM method was proposed.•The recognition accuracy for mild mental stress increased from 88.57% to 98.57%.•The Energy feature is more discriminating compared with ...other texture features.
Mild mental stress, which represents the type of stress that occurs in daily life without a specific time limit, can accumulate and eventually lead to many physical and psychological disorders. Moreover, the overall heart rate fluctuations induced by mild mental stress are less significant, posing a great challenge to stress detection. However, current traditional methods have the drawback of limited ability to quantify local details and spatial variations in the pattern of autonomic fluctuations during early mental stress. To address these issues, in this study, a novel inter-beat interval-based analysis model is proposed, which is comprised of sequence coding-based transformation and construction of gray level co-occurrence matrix (GLCM). The inter-beat intervals were first mapped into binary symbolic sequences, and then the word sequences were constructed. Then four texture features, i.e., Contrast, Correlation, Energy, and Homogeneity, were extracted from the derived GLCM constructed from the word sequences. Additionally, other classic multi-domain features were also extracted. Statistical analysis and Spearman correlation analysis were then performed, respectively. After feature selection by recursive feature elimination, different feature subsets were fed into the support vector machine to identify whether subjects were suffering from mental stress. The results showed that Energy was not only statistically different but also associated with altered mental states. The accuracy using only classic features was 88.57%, while combining classic and texture features increased the highest accuracy to 98.57%. This study provides promising methodology to detecting mild mental stress and potential insight into the mechanisms underlying its pathophysiology.
•Estimation of blood pressure values using pulse rate variability features shows promise for the continuous, non-invasive measurement of systolic, diastolic, and mean arterial pressure.•Using ...photoplethysmography-based pulse rate variability features only, it is possible to classify hypertensive events in critically ill subjects with relatively good performance. However, the classification of hypertensive and normotensive events is still a challenge.•Using 5-min windows for the classification and estimation of blood pressure in critically ill subjects using solely pulse rate variability features gives a better performance than using 1-min windows.
Objective: The aim of this study was to evaluate the capability of features extracted from photoplethysmography (PPG) based Pulse Rate Variability (PRV) to classify hypertensive, normotensive and hypotensive events, and to estimate mean arterial, systolic and diastolic blood pressure in critically ill patients. Methods: Time-domain, frequency-domain and non-linear indices from PRV were extracted from 5-min and 1-min segments obtained from PPG signals. These features were filtered using machine learning algorithms in order to obtain the optimal combination for the classification of hypertensive, hypotensive and normotensive events, and for the estimation of blood pressure. Results: 5-min segments allowed for an improved performance in both classification and estimation tasks. Classification of blood pressure states showed around 70% accuracy and around 75% specificity. The sensitivity, precision and F1 scores were around 50%. In estimating mean arterial, systolic, and diastolic blood pressure, mean absolute errors as low as 2.55 ± 0.78 mmHg, 4.74 ± 2.33 mmHg, and 1.78 ± 0.14 mmHg were obtained, respectively. Bland-Altman analysis and Wilcoxon rank sum tests showed good agreement between real and estimated values, especially for mean and diastolic arterial blood pressures. Conclusion: PRV-based features could be used for the classification of blood pressure states and the estimation of blood pressure values, although including additional features from the PPG waveform could improve the results. Significance: PRV contains information related to blood pressure, which may aid in the continuous, noninvasive, non-intrusive estimation of blood pressure and detection of hypertensive and hypotensive events in critically ill subjects.
Pulse rate variability (PRV) describes the changes in pulse rate through time, when measured using pulsatile signals such as photoplethysmograms (PPG). PRV has been used as a surrogate of heart rate ...variability (HRV), but their relationship is not straightforward, both due to physiological differences and to effects of technical aspects on the extraction of PRV information from pulsatile signals such as the PPG. One of the factors that may affect PRV analysis is the presence of noise and the filtering strategy used to pre-process the PPG signal. In this study, the aim was to evaluate the best performing filtering strategy for the extraction of PRV information reliably from noise-contaminated synthetic PPG signals. Time domain, frequency domain and Poincaré plot indices were extracted from PRV trends obtained from the filtered PPG signals and compared against indices measured from a gold-standard simulated PRV function. It was found that PRV information can be reliably extracted from PPG signals filtered using lower low cutoff frequencies and elliptic IIR or equiripple or Parks–McClellan FIR filters, however the filtering parameters depend on the type of noise present in the signal. Moreover, special care should be taken to assess the pNN50 index from contaminated PPG signals, regardless of the type of noise. Future studies should aim to validate these results from real PPG data.
•A simulation framework is proposed for the generation of noise-contaminated photoplethysmographic signals with varying pulse rate variability information.•The best performing filtering strategies were determined for each type of noise and for each PRV index.•Best results were obtained when PPG signals were filtered using lower low cut-off frequencies and elliptic, equiripple or Parks–McClellan filters.
Photoplethysmography is an optical technique that produces a wealth of information about cardiovascular health. Therefore, the technology has become an integral part of personal health monitoring ...devices. Given the importance of blood pressure measurement and control in physical and mental health, in recent years, the estimation of blood pressure from photoplethysmography has been an active area of research with promising results. Most studies on the subject rely on the morphological features of the photoplethysmogram. These features are highly prone to noise, changes in sensor placement, and skin properties; including skin colour. To address these limitations, we investigated the feasibility of using pulse rate variability features which are known to be less prone to the aforementioned limitations. To this end, we collected high quality photoplethysmograms using a bespoke, research-grade device from 18 healthy subjects. Approximately 15 min of photoplethysmograms and continuous blood pressure waveforms were collected from each subject. We trained machine learning models based on different feature sets and compared their performances. The model with morphological features alone outperformed the model with pulse rate variability features, root mean squared error (RMSE) of 6.32 vs 7.23 mmHg. However, the best performance was obtained using the combined set of features (RMSE: 5.71 mmHg). Combined, the evidence shows that the estimation of BP from PRV, alone or in conjunction with morphological features, is feasible. In light of the limitations of morphological features in estimation of blood pressure, our findings lend support to further research on the use of pulse rate variability features.
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•Compared to the morphological features of the photoplethysmogram (PPG), pulse rate variability (PRV) features are less sensitive to noise, sensor placement, and skin properties such as colour.•Estimation of blood pressure from pulse rate variability features can remedy some of the shortcomings of photoplethysmography-based blood pressure monitoring devices.•Incorporation of pulse rate variability features in the estimation of blood pressure improves the predictive performance.
Pulse rate variability (PRV) analysis appears as the first alternative to heart rate variability analysis for wearable devices; however, there is a constraint on computational load and energy ...consumption for the limited system resources available to the devices. Considering that adjustment of the sampling frequency is one of the strategies for reducing computational load and power consumption, this study aimed to investigate the influence of sampling frequency (fs) on PRV analysis and to find the minimum sampling frequency while maintaining reliability. We generated 5000, 2500, 1000, 500, 250, 100, 50, 25, 20, 15, 10, 5 Hz down-sampled photoplethysmograms from 10 kHz-sampled PPGs and derived time- and frequency-domain variables of the PRV. These included AVNN, SDNN, SDSD, RMSSD, NN50, pNN50, total power, VLF, LF, HF, LF/HF, nLF and nHF for each down-sampled signal. Derived variables were compared with heart rate variability of the 10 kHz-sampled electrocardiograms, and then statistically investigated using one-way ANOVA test and Bland-Altman analysis. As a result, significant differences (P < 0.05) were found for SDNN, SDSD, RMSSD, NN50, pNN50, TP, HF, LF/HF, nLF and nHF, but not for AVNN, VLF and LF. Based on the post hoc tests, it was found that the NN50 and pNN50, SDSD and RMSSD, LF/HF and nHF, SDNN, TP and nLF analysis had significant differences at fs 20 Hz, fs 15 Hz, fs 10 Hz; fs = 5 Hz, respectively. In other words, a significant difference was not seen for any variable if the fs was greater than 25 Hz. Consequently, our pilot study suggests that analysis of variability in the time and frequency domain from pulse rate obtained through PPG may be potentially as reliable as that derived from the analysis of the electrocardiogram, provided that fs 25 Hz sampling frequency is used.
•The difference in current and preceding PAT explains the discrepancy of RRI and PPI.•The PAT oscillates and is likely coupled to respiration.•HRV and PRV measures inherently differ, due to ...differences between two consecutive PATs.•The RP% of PAT oscillation is minimal at ½ amplitude of the PPG and maximal at base point.•Momentary PAT and the steepness of PPG change inversely, resulting in compensation.
ECG-based heart rate variability (HRV), and more recently, optical pulse rate variability (PRV) analysis used in wearables are non-invasive tools of neurocardiac investigation. Pulse arrival time (PAT) is derived from their simultaneous recording. We examined the beat-to-beat PAT in greater detail, while analyzed the PRV to HRV relationship.
We acquired 300sec ECG, photoplethysmogram (PPG), and respiratory-signals from 35 young, healthy volunteers while in a supine position including different breathing patterns. PAT at eight reference points on the PPG-side was assessed by the mean, relative precision (RP%) and spectral analysis. PRV and HRV parameters were compared by relative accuracy error (RAE) and the Bland-Altman-Ratio (BAR).
PAT showed the minimum RP% at the 1/2-amplitude point whereas RP% reached the maximum at the base point; its observed fine oscillation was associated to breathing as confirmed through spectral analysis. The instantaneous slope of PPG rise is inversely proportional to the corresponding PAT. RAE and BAR showed excellent agreement in 15 of 16 analysis at time-domain, mostly excellent or moderate on frequency-domain and nonlinear analysis. The inherent difference between HRV and PRV is formally due to the difference among two consecutive PATs.
The present study supported the interchangeability of HRV- and PRV-analysis. Our formalistic explanation linking successive PAT-pairs and the inborn difference between HRV and PRV may bear further implications in selecting the appropriate fiducial points, approximating PRV to HRV measures, and in creating innovative neurocardiac parameters or complex PPG models/simulators.
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