-
Scientific Reports Nov 2023Associations between cerebrovascular disease and impaired autonomic function and cerebrovascular reactivity have led to increased interest in variability of heart rate...
Associations between cerebrovascular disease and impaired autonomic function and cerebrovascular reactivity have led to increased interest in variability of heart rate (HRV) and blood pressure (BPV) following stroke. In this study, beat-to-beat pulse rate variability (PRV) and BPV were measured in clinically stable stroke patients (6 ischemic, 2 hemorrhagic) at least one year after their last cerebrovascular event. Beat-to-beat blood pressure (BP) measurements were collected from subjects while resting in the sitting position for one hour. Compared with healthy controls, stroke patients exhibited significantly greater time-domain (standard deviation, coefficient of variation, average real variability) and normalized high-frequency BPV (all p < 0.05). Stroke patients also exhibited lower LF:HF ratios than control subjects (p = 0.003). No significant differences were observed in PRV between the two groups, suggesting that BPV may be a more sensitive biomarker of cerebrovascular function in long-term post-stroke patients. Given a paucity of existing literature investigating beat-to-beat BPV in clinically stable post-stroke patients long (> 1 year) after their cerebrovascular events, this pilot study can help inform future studies investigating the mechanisms and effects of BPV in stroke. Elucidating this physiology may facilitate long-term patient monitoring and pharmacological management to mitigate the risk for recurrent stroke.
Topics: Humans; Blood Pressure; Heart Rate; Pilot Projects; Stroke; Monitoring, Physiologic
PubMed: 37935766
DOI: 10.1038/s41598-023-45479-4 -
Computational and Mathematical Methods... 2021Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are...
BACKGROUND
Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are restricted by the signal quality of pulse wave. Time synchronous averaging is a powerful noise reduction method for periodic and approximately periodic signals. It is usually used to extract single-period pulse waveforms, but has nothing to do with pulse rate variability monitoring and atrial fibrillation detection traditionally. If this method is improved properly, it may provide a new way to measure pulse rate variability and to detect atrial fibrillation, which may have some potential advantages under the condition of poor signal quality.
OBJECTIVE
The objective of this paper was to develop a new measure of pulse rate variability by improving existing time synchronous averaging and to detect atrial fibrillation by the new measure of pulse rate variability.
METHODS
During time synchronous averaging, two adjacent periods were regarded as the basic unit to calculate the average signal, and the difference between waveforms of the two adjacent periods was the new measure of pulse rate variability. 3 types of distance measures (Euclidean distance, Manhattan distance, and cosine distance) were tested to measure this difference on a simulated training set with a capacity of 1000. The distance measure, which can accurately distinguish regular pulse rate and irregular pulse rate, was used to detect atrial fibrillation on the testing set with a capacity of 62 (11 with atrial fibrillation, 8 with premature contraction, and 43 with sinus rhythm). The receiver operating characteristic curve was used to evaluate the performance of the indexes.
RESULTS
The Euclidean distance between waveforms of the two adjacent periods performs best on the training set. On the testing set, the Euclidean distance in atrial fibrillation group is significantly higher than that of the other two groups. The area under receiver operating characteristic curve to identify atrial fibrillation was 0.998. With the threshold of 2.1, the accuracy, sensitivity, and specificity were 98.39%, 100%, and 98.04%, respectively. This new index can detect atrial fibrillation from pulse wave signal.
CONCLUSION
This algorithm not only provides a new perspective to detect AF but also accomplishes the monitoring of PRV and the extraction of single-period pulse wave through the same technical route, which may promote the popularization and application of pulse wave.
Topics: Algorithms; Analysis of Variance; Atrial Fibrillation; Computational Biology; Diagnosis, Computer-Assisted; Heart Rate; Humans; Machine Learning; Pulse Wave Analysis; ROC Curve; Radial Artery; Wearable Electronic Devices
PubMed: 33868451
DOI: 10.1155/2021/5597559 -
PloS One 2022Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely...
BACKGROUND
Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions.
OBJECTIVE
We evaluate the accuracy of PPG signals-collected by the Samsung Gear Sport smartwatch in free-living conditions-in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor.
METHODS
We conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods.
RESULTS
We found a significantly high positive correlation between the smartwatch's and Shimmer ECG's HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch's and shimmer ECG's LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances.
CONCLUSION
The Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.
Topics: Female; Male; Humans; Heart Rate; Correlation of Data; Exercise
PubMed: 36480505
DOI: 10.1371/journal.pone.0268361 -
Journal of Physiological Anthropology Feb 2020Recently, attempts have been made to use the pulse rate variability (PRV) as a surrogate for heart rate variability (HRV). PRV, however, may be caused by the...
BACKGROUND
Recently, attempts have been made to use the pulse rate variability (PRV) as a surrogate for heart rate variability (HRV). PRV, however, may be caused by the fluctuations of left ventricular pre-ejection period and pulse transit time besides HRV. We examined whether PRV differs not only from HRV but also depending on the measurement site.
RESULTS
In five healthy subjects, pulse waves were measured simultaneously on both wrists and both forearms together with single-lead electrocardiogram (ECG) in the supine and sitting positions. Although average pulse interval showed no significant difference from average R-R interval in either positions, PRV showed greater power for the low-frequency (LF) and high-frequency (HF) components and lower LF/HF than HRV. The deviations of PRV from HRV in the supine and sitting positions were 13.2% and 7.9% for LF power, 24.5% and 18.3% for HF power, and - 15.0% and - 30.2% for LF/HF, respectively. While the average pulse interval showed 0.8% and 0.5% inter-site variations among the four sites in the supine and sitting positions, respectively, the inter-site variations in PRV were 4.0% and 3.6% for LF power, 3.8% and 4.7% for HF power, and 18.0% and 17.5% for LF/HF, respectively.
CONCLUSIONS
These suggest that PRV shows not only systemic differences from HRV but also considerable inter-site variations.
Topics: Adult; Electrocardiography; Female; Forearm; Heart Rate; Humans; Male; Posture; Pulse Wave Analysis; Signal Processing, Computer-Assisted; Wearable Electronic Devices; Wrist; Young Adult
PubMed: 32085811
DOI: 10.1186/s40101-020-0214-1 -
Sensors (Basel, Switzerland) Sep 2021Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is...
Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual's autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.
Topics: Algorithms; Autonomic Nervous System; Fingers; Heart Rate; Photoplethysmography; Pulse; Signal Processing, Computer-Assisted
PubMed: 34577448
DOI: 10.3390/s21186241 -
Sensors (Basel, Switzerland) May 2021Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV...
Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP's PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP and ECG over 15 opportunities. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP's proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10-11%) and SWC (5-5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP's proprietary filter, which approached or exceeded the CV (3-13%) and SWC (1.5-6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.
Topics: Electrocardiography; Heart Rate; Photoplethysmography; Wrist; Wrist Joint
PubMed: 34065516
DOI: 10.3390/s21103571 -
Sensors (Basel, Switzerland) Dec 2021Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR)...
Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.
Topics: Algorithms; Heart Rate; Monitoring, Physiologic; Radar; Respiration; Respiratory Rate; Signal Processing, Computer-Assisted; Vital Signs
PubMed: 35009628
DOI: 10.3390/s22010083 -
Sensors (Basel, Switzerland) Dec 2023Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted...
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.
Topics: Photoplethysmography; Algorithms; Artifacts; Electrocardiography; Heart Rate
PubMed: 38203003
DOI: 10.3390/s24010141 -
The Journal of Clinical Pediatric... 2018We aimed to examine if changes in oxygen saturation and pulse rate of pediatric patients during conscious sedation with midazolam and nitrous oxide are associated with...
OBJECTIVE
We aimed to examine if changes in oxygen saturation and pulse rate of pediatric patients during conscious sedation with midazolam and nitrous oxide are associated with child's behavior, midazolam dose, the type and duration of the treatment and demographic parameters.
STUDY DESIGN
This study was a retrospective chart review of consecutive pediatric patients, aged 2.5-12.5 years, who had undergone conscious sedation for dental treatment with oral midazolam (with or without nitrous oxide) between January 2011 and September 2015 at the Department of Pediatric Dentistry of Tel Aviv University. Oral midazolam was administered according to the patients' weight, either at 0.4 mg/kg, 0.5 mg/kg or at a maximum dose of 10 mg. In all cases pulse rate and oxygen saturation were monitored every 15 minutes during treatment, Results: 147 sedation sessions (82 of females and 65 of males) were included in the study. Sedation was successful in 80% of cases. Children with poor behavior scores had statistically significant different mean saturation levels, albeit within normal range, during the treatment (p<0.012) as well as a clinically significant higher mean pulse rate (p=0.0001), compared to children with good or excellent behavior scores. Treatment duration, the type of dental procedure or the patients' weight were not correlated with the change in oxygen saturation or pulse rate during the treatment.
CONCLUSIONS
Poor behavior of pediatric patients does not affect oxygen saturation, but it increases the pulse rate of children under sedation with midazolam and nitrous oxide.
Topics: Administration, Oral; Anesthesia, Dental; Anesthetics, Inhalation; Child; Child Behavior; Child, Preschool; Conscious Sedation; Female; Heart Rate; Humans; Hypnotics and Sedatives; Male; Midazolam; Nitrous Oxide; Oxygen; Retrospective Studies
PubMed: 30085872
DOI: 10.17796/1053-4625-42.6.11 -
Scientific Reports Apr 2022This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously...
This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously extracted using THz waves based on the established principles in electronics and optics. The first fundamental principle is micro-Doppler motion effect. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave) for breathe rate and heart rate detection. The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). Herein, we introduce the concept of terahertz-wave-plethysmography (TPG), which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle. The TPG principle is justified by scientific deduction, electromagnetic wave simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by the Terahertz Electronics Lab at Arizona State University, Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and standard deviation 1.08 BPM. The results validate the feasibility of TPG for direct pulse monitoring. A comparative study on pulse sensitivity is conducted between TPG and rPPG. The results indicate that the TPG contains more pulsatile information from the forehead BOI than that in the rPPG signals in regular office lighting condition and thus generate better heart rate estimation statistic in the form of empirical cumulative distribution function of HR estimation error. Last but not least, TPG penetrability test for covered skin is demonstrated using two types of garment materials commonly used in daily life.
Topics: Heart Rate; Humans; Photoplethysmography; Plethysmography; Pulse; Radar
PubMed: 35428772
DOI: 10.1038/s41598-022-09801-w