Physiological coherence has been related with a general sense of well‐being and improvements in health and physical, social, and cognitive performance. The aim of this study was to evaluate the ...relationship between acute stress, controlled breathing, and physiological coherence, and the degree of body systems synchronization during a coherence‐generation exercise. Thirty‐four university employees were evaluated during a 20‐min test consisting of four stages of 5‐min duration each, during which basal measurements were obtained (Stage 1), acute stress was induced using validated mental stressors (Stroop test and mental arithmetic task, during Stage 2 and 3, respectively), and coherence states were generated using a controlled breathing technique (Stage 4). Physiological coherence and cardiorespiratory synchronization were assessed during each stage from heart rate variability, pulse transit time, and respiration. Coherence measurements derived from the three analyzed variables increased during controlled respiration. Moreover, signals synchronized during the controlled breathing stage, implying a cardiorespiratory synchronization was achieved by most participants. Hence, physiological coherence and cardiopulmonary synchronization, which could lead to improvements in health and better life quality, can be achieved using slow, controlled breathing exercises. Meanwhile, coherence measured during basal state and stressful situations did not show relevant differences using heart rate variability and pulse transit time. More studies are needed to evaluate the ability of coherence ratio to reflect acute stress.
With the continued development and rapid growth of wearable technologies, PPG has become increasingly common in everyday consumer devices such as smartphones and watches. There is, however, minimal ...knowledge on the effect of the contact pressure exerted by the sensor device on the PPG signal and how it might affect its morphology and the parameters being calculated. This study explores a controlled in vitro study to investigate the effect of continually applied contact pressure on PPG signals (signal-to-noise ratio (SNR) and 17 morphological PPG features) from an artificial tissue-vessel phantom across a range of simulated blood pressure values. This experiment confirmed that for reflectance PPG signal measurements for a given anatomical model, there exists an optimum sensor contact pressure (between 35.1 mmHg and 48.1 mmHg). Statistical analysis shows that temporal morphological features are less affected by contact pressure, lending credit to the hypothesis that for some physiological parameters, such as heart rate and respiration rate, the contact pressure of the sensor is of little significance, whereas the amplitude and geometric features can show significant change, and care must be taken when using morphological analysis for parameters such as SpO2 and assessing autonomic responses.
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.
Pulse rate variability (PRV) refers to the changes in pulse rate through time and is extracted from pulsatile signals such as the photoplethysmogram (PPG). Although PRV has been used as a surrogate ...of heart rate variability (HRV), which is measured from the electrocardiogram (ECG), these variables have been shown to have differences, and it has been hypothesised that these differences may arise from technical aspects that may affect the reliable extraction of PRV from PPG signals. Moreover, there are no guidelines for the extraction of PRV information from pulsatile signals.
In this study, the extraction of frequency-domain information from PRV was studied, in order to establish the best performing combination of parameters and algorithms to obtain the spectral representation of PRV.
PPG signals with varying and known PRV content were simulated, and PRV information was extracted from these signals. Several spectral analysis techniques with different parameters were applied, and absolute, relative and centroid-related frequency-domain indices extracted from each combination. Indices from extracted and known PRV were compared using factorial analyses and Kruskal-Wallis tests to determine which spectral analysis technique gave the best performing results.
It was found that using fast Fourier transform and the multiple signal classification (PMUSIC) algorithms gave the best results, combined with cubic spline interpolation and a frequency resolution of 0.0078 Hz for the former; and a linear interpolation with a frequency resolution as low as 1.22 × 10
, as well as applying a fifth order model, for the latter.
Considering the lower complexity of FFT over PMUSIC, FFT should be considered as the appropriate technique to extract frequency-domain information from PRV signals.
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.
Pulse rate variability (PRV) assesses the changes in pulse rate through time when pulse rate is extracted from pulsatile signals such as the photoplethysmogram (PPG). PRV has been used as a surrogate ...of heart rate variability (HRV), but there is evidence of differences between these two variables. It has been hypothesised that these differences may arise from physiological processes or from technical aspects that may affect the reliable extraction of PRV indices from PPG signals. Moreover, there are no guidelines for the extraction of PRV information from pulsatile signals, which hinders the comparison among PRV studies and the understanding of physiological changes that may affect PRV. In this study, the effects of using PPG signals with different duration for the extraction of time-domain, frequency-domain and Poincaré plot indices from PRV was studied. Using simulated PPG signals with known PRV content and varying duration, it was found that PRV indices can be reliably estimated from signals as short as 90 s. This indicates that PRV indices can be extracted from ultra-short PPG signals. Although further validation with real data is needed, it can be concluded that acquiring shorter segments of PPG can be used for PRV analysis, allowing for a more efficient acquisition and processing of this variable.
•A simulation framework is proposed for generation of PPG signals and PRV information.•The minimum length of PPG signals for PRV analysis is determined for each of index.•The minimum PPG length was found to be lower than usually accepted for PRV analysis.
•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.
•A model for the simulation of photoplethysmographic signals, with varying pulse rate variability information, is proposed.•The selection of interbeat intervals detection algorithm and fiducial ...points alters the results of pulse rate variability when measured from photoplethysmographic signals. In general, it was found that more reliable results can be obtained when onset-related fiducial points and algorithms were used.•The sampling rate used to acquire the photoplethysmographic signals needs to be carefully selected for the assessment of pulse rate variability, according to the indices that are to be analyzed. However, sampling rates lower than 256 Hz are sufficient for assessing most time-domain and Poincaré plot indices.
Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content.
PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests.
From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz.
The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application.
The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.
Objective:
This research aims to evaluate the possible association between pulsatile near infrared spectroscopic waveform features and induced changes in intracranial pressure in healthy volunteers.
...Methods:
An optical intracranial pressure sensor was attached to the forehead of 16 healthy volunteers. Pulsatile near infrared spectroscopic signals were acquired from the forehead during body position changes and Valsalva manoeuvers. Features were extracted from the pulsatile signals and analyses were carried out to investigate the presence of statistical differences in the features when intracranial pressure changes were induced. Classification models were developed utilizing the features extracted from the pulsatile near-infrared spectroscopic signals to classify between different body positions and Valsalva manoeuvre.
Results:
The presence of significant differences in the majority of the analyzed features (p
<
0.05) indicates the technique’s ability to distinguish between variations in intracranial pressure. Furthermore, the disparities observed in the optical signal features captured by the proximal and distal photodetectors support the hypothesis that alterations in back-scattered light directly correspond to brain-related changes. Further research is required to subtract distal and proximal signals and construct predictive models employing a gold standard measurement for non-invasive, continuous monitoring of intracranial pressure.
Conclusion:
The study investigated the use of pulsatile near infrared spectroscopic signals to detect changes in intracranial pressure in healthy volunteers. The results revealed significant differences in the features extracted from these signals, demonstrating a correlation with ICP changes induced by positional changes and Valsalva manoeuvre. Classification models were capable of identifying changes in ICP using features from optical signals from the brain, with a sensitivity ranging from 63.07% to 80% and specificity ranging from 60.23% to 70% respectively. These findings underscored the potential of these features to effectively identify alterations in ICP.
Significance:
The study’s results demonstrate the feasibility of using features extracted from optical signals from the brain to detect changes in ICP induced by positional changes and Valsalva manoeuvre in healthy volunteers. This represents a first step towards the non-invasive monitoring of intracranial pressure.
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