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Cool dialysate has variable impact on hemodynamic stability and dialysis adequacy. Hemodynamic stability and dialysis adequacy are crucial indicators for better life expectancy and cardiovascular mortality. This research aims to evaluate the impact of cool dialysate temperature (35.5°C) compared to standard dialysate temperature (37°C) on blood pressures, pulse rate, and dialysis adequacy (Urea reduction ratio and online Kt/V) in a cross over design. Material and Methods. Consenting ESRD patients on maintenance haemodialysis (HD) with minimum 3 months dialysis vintage and functioning permanent vascular access are included for the study. Each participant had two sessions of HD at 37°C followed by two sessions at 35.5° C on a Fresenius 4008S HD machine. Systolic blood pressure (SBP), diastolic blood pressure (DBP) and Pulse rate are measured pre-HD, every hourly and post dialysis. Pre-HD Blood urea nitrogen (BUN) and post-HD BUN are measured, and Urea reduction rate was calculated for each HD session. Kt/V was calculated by ionic conductance by HD machine for each session. Results. 25 patients (5 females and 20 males) were enrolled. The mean age was 54 ± 9.58 years. Dialysis vintage was 21.48 ± 6.9 months for study participants 10 patients (40%) were diabetic nephropathy, 9 patients (36%) were presumed chronic glomerulonephritis, 2 patients (8%) were lupus nephritis and 4 patients (16%) were chronic interstitial nephritis. There was statistically no difference between pre-HD BUN (p = 0.330), post-HD BUN (p = 0.776), URR (p = 0.718) and Kt/V (p = 0.534) among the dialysis sessions done at 37°C and 35.5°C. SBP variability in the low temperature (35.5°C) group at 4th hour and post dialysis assumed statistical significance with p = 0.05 and p = 0.025 respectively. DBP variability in the low temperature (35.5°C) group at 3rd hour, 4th hour and post-dialysis demonstrated statistical significance with p = 0.027, p = 0.36 and p = 0.016 respectively. Pulse rate variability was more in the low temperature (35.5°C) group at 3rd hour and 4th hour which showed statistical significance with p = 0.037 and p = 0.05 respectively. Conclusion. Cool dialysate is non inferior to standard dialysate temperature in terms of dialysis adequacy and is associated with less variability in diastolic blood pressure, systolic blood pressure and more pulse rate variability thereby contributing to better hemodynamic stability.

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Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20-100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland-Altman plots and coefficient of variation (CoV). A sampling frequency of ~100-200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40-50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1-8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates.

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Slow paced breathing has been demonstrated to provide significant health benefits for a person's health, and, during breathing sessions, it is desirable to monitor that a person is actually compliant with the breath pacer. We explore the potential use of pulse rate variability to monitor compliance with a breath pacer during meditation sessions. The study involved 6 human subjects each participating in 2-3 trials, where they are asked to follow or not to follow the breath pacer, where we collected data on how the magnitude of pulse rate variability changed. Two methods, logistic regression and a running standard deviation technique, were developed to detect non-compliance with the breath pacer based on pulse rate variability metrics. Results indicate that using pulse rate variability alone may not reliably detect non-compliance with the breath pacer. Both models exhibited limitations in terms of false positives and false negatives, with accuracy ranging from 67 to 65%. Existing methods involving visual, audio, and motion signals currently perform better for monitoring compliance with the breath pacer.

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The aim of this study is to analyze the performance of classifying stress and non-stress by measuring biosignal data using a wearable watch without interfering with work activities at work. An experiment is designed where participants wear a Galaxy Watch3 to measure HR and photoplethysmography data while performing stress-inducing and relaxation tasks. The classification model was constructed using k-NN, SVM, DT, LR, RF, and MLP classifiers. The performance of each classifier was evaluated using LOSO-CV as a verification method. When the top 9 features, including the average and minimum value of HR, average of NNI, SDNN, vLF, HF, LF, LF/HF ratio, and total power, were used in the classification model, it showed the best performance with an accuracy of 0.817 and an F1 score of 0.801. This study also finds that it is necessary to measure physiological data for more than 2 or 3 min to accurately distinguish stress states.

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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.

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Background: Research on the compatibility of time domain indices, frequency domain measurements of heart rate variability obtained from electrocardiogram (ECG) waveforms, and pulse wave signal (pulse rate variability; PRV) features is ongoing. The promising marker of cardiac autonomic function is heart rate variability. Recent research has looked at various other physiological markers, leading to the emergence of pulse rate variability. The pulse wave signal can be studied for variations to understand better changes in arterial stiffness and compliance, which are key indicators of cardiovascular health. Methods: 35 healthy overweight people were included. The Lead II electrocardiogram (ECG) signal was transmitted through an analog-to-digital converter (PowerLab 8/35 software, AD Instruments Pty. Ltd., New South Wales, Australia). This signal was utilized to compute Heart Rate Variability (HRV) and was sampled at a rate of 1024 Hz. The same AD equipment was also used to capture a pulse signal simultaneously. The right index finger was used as the recording site for the pulse signal using photoplethysmography (PPG) technology. Results: The participants' demographic data show that the mean age was 23.14 + 5.27 years, the mean weight was 73.68 +  7.40 kg, the mean body fat percentage was 32.23   +  5.30, and the mean visceral fat percentage was 4.60   +  2.0. The findings revealed no noticeable difference between the median values of heart rate variability (HRV) and PRV. Additionally, a strong correlation was observed between HRV and PRV. However, poor agreement was observed in the measurement of PRV and HRV. Conclusion: All indices of HRV showed a greater correlation with PRV. However, the level of agreement between HRV and PRV measurement was poor. Hence, HRV cannot be replaced with PRV and vice-versa.

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OBJECTIVES: Pulse rate variability (PRV) predicts stroke in patients with sleep disordered breathing (SDB). However, the relationship between PRV and cardiovascular disease (CVD) was unknown in SDB. METHODS: This was a cross-sectional study. Community residents in Guangdong were investigated. Sleep study were conducted with a type Ⅳ sleep monitoring. PRV parameters was assessed from the pulse waveforms derived from the sleep monitoring. RESULTS: 3747 participants were enrolled. The mean age was 53.9 ± 12.7 years. 1149 (30.7%) were diagnosed as SDB. PRV parameters, except for the averages of pulse-to-pulse intervals (ANN), were higher in participants with SDB than those without. After adjusting for traditional CVD risk factors, deceleration capacity of rate (DC), ANN, and the percentage of pulse-to-pulse interval differences that were more than 50 ms (PNN50) were correlated with CVD risk in participants with SDB (OR were 0.826, 1.002, and 1.285; P were 0.003, 0.009, and 0.010), but not in participants without SDB. There was no interaction effect between DC, ANN, PNN50 and oxygen desaturation index. In hierarchical analysis, DC and ANN were predictors for CVD in SDB patients with age <60 years, male, overweight, diabetes, and normal lipid metabolism. PNN50 was predictor for CVD in the elderly SDB patients without overweight, diabetes or dyslipidemia. CONCLUSIONS: PRV parameters may be specific predictors for CVD in SDB. PNN50 was a potent biomarker for CVD risk in the elderly with SDB, event without traditional CVD risk factors.

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Stress is a direct or indirect cause of reduced work efficiency in daily life. It can damage physical and mental health, leading to cardiovascular disease and depression. With increased interest and awareness of the risks of stress in modern society, there is a growing demand for quick assessment and monitoring of stress levels. Traditional ultra-short-term stress measurement classifies stress situations using heart rate variability (HRV) or pulse rate variability (PRV) information extracted from electrocardiogram (ECG) or photoplethysmography (PPG) signals. However, it requires more than one minute, making it difficult to monitor stress status in real-time and accurately predict stress levels. In this paper, stress indices were predicted using PRV indices acquired at different lengths of time (60 s, 50 s, 40 s, 30 s, 20 s, 10 s, and 5 s) for the purpose of real-time stress monitoring. Stress was predicted with Extra Tree Regressor, Random Forest Regressor, and Gradient Boost Regressor models using a valid PRV index for each data acquisition time. The predicted stress index was evaluated using an R2 score between the predicted stress index and the actual stress index calculated from one minute of the PPG signal. The average R2 score of the three models by the data acquisition time was 0.2194 at 5 s, 0.7600 at 10 s, 0.8846 at 20 s, 0.9263 at 30 s, 0.9501 at 40 s, 0.9733 at 50 s, and 0.9909 at 60 s. Thus, when stress was predicted using PPG data acquired for 10 s or more, the R2 score was confirmed to be over 0.7.

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Background and Objectives: To compare autonomic and vascular responses during reactive hyperemia (RH) between healthy individuals and patients with sickle cell anemia (SCA). Materials and Methods: Eighteen healthy subjects and 24 SCA patients were subjected to arterial occlusion for 3 min at the lower right limb level. The pulse rate variability (PRV) and pulse wave amplitude were measured through photoplethysmography using the Angiodin® PD 3000 device, which was placed on the first finger of the lower right limb 2 min before (Basal) and 2 min after the occlusion. Pulse peak intervals were analyzed using time-frequency (wavelet transform) methods for high-frequency (HF: 0.15-0.4) and low-frequency (LF: 0.04-0.15) bands, and the LF/HF ratio was calculated. Results: The pulse wave amplitude was higher in healthy subjects compared to SCA patients, at both baseline and post-occlusion (p < 0.05). Time-frequency analysis showed that the LF/HF peak in response to the post-occlusion RH test was reached earlier in healthy subjects compared to SCA patients. Conclusions: Vasodilatory function, as measured by PPG, was lower in SCA patients compared to healthy subjects. Moreover, a cardiovascular autonomic imbalance was present in SCA patients with high sympathetic and low parasympathetic activity in the basal state and a poor response of the sympathetic nervous system to RH. Early cardiovascular sympathetic activation (10 s) and vasodilatory function in response to RH were impaired in SCA patients.

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Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

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The conventional approach to monitoring sleep stages requires placing multiple sensors on patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single-sensor photoplethysmographic (PPG)-based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of 10 patients. Data analysis was performed to obtain 79 features from the recordings, which were then classified according to sleep stages. The classification results using support vector machine (SVM) with the polynomial kernel yielded an overall accuracy of 84.66%, 79.62% and 72.23% for two-, three- and four-stage sleep classification. These results show that it is possible to conduct sleep stage monitoring using only PPG. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.

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Heart rate variability (HRV) is measured to analyze autonomic nervous system function in humans, and pulse rate variability (PRV) assessed using the photoplethysmography method with a pulse oximeter has been proposed as a surrogate for HRV. To examine whether PRV is compatible with HRV in patients with chronic obstructive pulmonary disease (COPD), we simultaneously measured HRV with an electrocardiogram and PRV with a pulse oximeter in patients with COPD, and compared low-frequency and high-frequency components computed from HRV and PRV as indicators of autonomic nervous system function. In a Bland-Altman analysis, the low-frequency component computed from HRV exhibited good consistency with that computed from PRV. The high-frequency component showed a significant fixed error but relatively good consistency. Our results indicate that autonomic nervous system function may be estimated with the low-frequency component by measuring PRV with a pulse oximeter in patients with COPD.

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Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method. The experimental results on simulated data display that the proposed improved sample entropy can improve the operation rate of the entropy value by a maximum of 47.6 times and an average of 28.6 times and keep the entropy value unchanged. Experimental results on PRV signal display that the proposed improved sample entropy has great potential in the real-time processing of physiological signals, which can increase approximately 35 times.

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Pulse rate variability (PRV), derived from Laser Doppler flowmetry (LDF) or photoplethysmography, has recently become widely used for sleep state assessment, although it cannot identify all the sleep stages. Peripheral blood flow (BF), also estimated by LDF, may be modulated by sleep stages; however, few studies have explored its potential for assessing sleep state. Thus, we aimed to investigate whether peripheral BF could provide information about sleep stages, and thus improve sleep state assessment. We performed electrocardiography and simultaneously recorded BF signals by LDF from the right-index finger and ear concha of 45 healthy participants (13 women; mean age, 22.5 ± 3.4 years) during one night of polysomnographic recording. Time- and frequency-domain parameters of peripheral BF, and time-domain, frequency-domain, and non-linear indices of PRV and heart rate variability (HRV) were calculated. Finger-BF parameters in the time and frequency domains provided information about different sleep stages, some of which (such as the difference between N1 and rapid eye movement sleep) were not revealed by finger-PRV. In addition, finger-PRV patterns and HRV patterns were similar for most parameters. Further, both finger- and ear-BF results showed 0.2-0.3 Hz oscillations that varied with sleep stages, with a significant increase in N3, suggesting a modulation of respiration within this frequency band. These results showed that peripheral BF could provide information for different sleep stages, some of which was complementary to the information provided by PRV. Furthermore, the combination of peripheral BF and PRV may be more advantageous than HRV alone in assessing sleep states and related autonomic nervous activity.

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Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a challenge. In this study, eight image-based photoplethysmography (iPPG) extraction methods (GRD, AGRD, PCA, ICA, LE, SPE, CHROM, and POS) were compared in terms of pulse rate (PR) and PRV features. The algorithms were made robust for motion and illumination artifacts by using ad hoc pre- and postprocessing steps. Then, they were systematically tested on the public dataset UBFC-RPPG, containing data from 42 subjects sitting in front of a webcam (30 fps) while playing a time-sensitive mathematical game. The performances of the algorithms were evaluated by statistically comparing iPPG-based and finger-PPG-based PR and PRV features in terms of Spearman's correlation coefficient, normalized root mean square error (NRMSE), and Bland-Altman analysis. The study revealed POS and CHROM techniques to be the most robust for PR estimation and the assessment of overall autonomic nervous system (ANS) dynamics by using PRV features in time and frequency domains. Furthermore, we demonstrated that a reliable characterization of the vagal tone is made possible by computing the Poincaré map of PRV series derived from the POS and CHROM methods. This study supports the use of iPPG systems as promising tools to obtain clinically useful and specific information about ANS dynamics.

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Heart rate variability (HRV) extracted from the electrocardiogram (ECG) is an essential indicator for assessing the autonomic nervous system in clinical. Some scholars have studied the feasibility of pulse rate variability (PRV) instead of HRV. However, there is little qualitative research in different body states. In this paper, the photoplethysmography (PPG) of postauricular and finger and the ECG of fifteen subjects were synchronously collected for comparative analysis. The eleven experiments were designed according to the daily living state, including the stationary state, limb movement state, and facial movement state. The substitutability of nine variables was investigated in the time, frequency, and nonlinearity domain by Passing Bablok regression and Bland Altman analysis. The results showed that the PPG of the finger was destroyed in the limb movement state. There were six variables of postauricular PRV, which showed a positive linear relationship and good agreement (p > 0.05, ratio ≤0.2) with HRV in all experiments. Our study suggests that the postauricular PPG could retain the necessary information of the pulse signal under the limb movement state and facial movement state. Therefore, postauricular PPG could be a better substitute for HRV, daily PPG detection, and mobile health than finger PPG.

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Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake-N1/N2/N3-REM) and 4 class (Wake-N1/N2-N3-REM) classification, with average κ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging.

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BACKGROUND: Visit-to-visit body weight variability (BWV), pulse rate variability (PRV), and blood pressure variability (BPV) have been respectively linked to multiple health outcomes. The associations of the combination of long-term variability in physiological measures with mortality and epigenetic age acceleration (EAA) remain largely unknown. METHODS: We constructed a composite score of physiological variability (0-3) of large variability in BWV, PRV, and BPV (the top tertiles) in 2006/2008-2014/2016 in the Health and Retirement Study (HRS) and 2011-2015 in the China Health and Retirement Longitudinal Study (CHARLS). All-cause mortality was documented through 2018. EAA was calculated using thirteen DNA methylation-based epigenetic clocks among 1047 participants in a substudy of the HRS. We assessed the relation of the composite score to the risk of mortality among 6566 participants in the HRS and 6906 participants in the CHARLS by Cox proportional models and then investigated its association with EAA using linear regression models. RESULTS: A higher score of variability was associated with higher mortality risk in both cohorts (pooled hazard ratio [HR] per one-point increment, 1.27; 95% confidence interval [CI], 1.18, 1.39; P-heterogeneity = 0.344), after adjustment for multiple confounders and baseline physiological measures. Specifically, each SD increment in BWV, PRV, and BPV was related to 21% (95% CI: 15%, 28%), 6% (0%, 13%), and 12% (4%, 19%) higher hazard of mortality, respectively. The composite score was significantly related to EAA in second-generation clocks trained on health outcomes (e.g., standardized coefficient = 0.126 in the Levine clock, 95% CI: 0.055, 0.196) but not in most first-generation clocks trained on chronological age. CONCLUSIONS: Larger variability in physiological measures was associated with a higher risk of mortality and faster EAA.

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Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2-120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38-100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26-1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.

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