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PeerJ 2022Heart rate and heart rate variability have enabled insight into a myriad of psychophysiological phenomena. There is now an influx of research attempting using these...
Heart rate and heart rate variability have enabled insight into a myriad of psychophysiological phenomena. There is now an influx of research attempting using these metrics within both laboratory settings (typically derived through electrocardiography or pulse oximetry) and ecologically-rich contexts ( wearable photoplethysmography, , smartwatches). However, these signals can be prone to artifacts and a low signal to noise ratio, which traditionally are detected and removed through visual inspection. Here, we developed an open-source Python package, RapidHRV, dedicated to the preprocessing, analysis, and visualization of heart rate and heart rate variability. Each of these modules can be executed with one line of code and includes automated cleaning. In simulated data, RapidHRV demonstrated excellent recovery of heart rate across most levels of noise (>=10 dB) and moderate-to-excellent recovery of heart rate variability even at relatively low signal to noise ratios (>=20 dB) and sampling rates (>=20 Hz). Validation in real datasets shows good-to-excellent recovery of heart rate and heart rate variability in electrocardiography and finger photoplethysmography recordings. Validation in wrist photoplethysmography demonstrated RapidHRV estimations were sensitive to heart rate and its variability under low motion conditions, but estimates were less stable under higher movement settings.
Topics: Heart Rate; Algorithms; Electrocardiography; Wrist; Photoplethysmography
PubMed: 35345583
DOI: 10.7717/peerj.13147 -
Journal of Physiological Anthropology Aug 2020With the popularization of pulse wave signals by the spread of wearable watch devices incorporating photoplethysmography (PPG) sensors, many studies are reporting the...
With the popularization of pulse wave signals by the spread of wearable watch devices incorporating photoplethysmography (PPG) sensors, many studies are reporting the accuracy of pulse rate variability (PRV) as a surrogate of heart rate variability (HRV). However, the authors are concerned about their research paradigm based on the assumption that PRV is a biomarker that reflects the same biological properties as HRV. Because PPG pulse wave and ECG R wave both reflect the periodic beating of the heart, pulse rate and heart rate should be equal, but it does not guarantee that the respective variabilities are also the same. The process from ECG R wave to PPG pulse wave involves several transformation steps of physical properties, such as those of electromechanical coupling and conversions from force to volume, volume to pressure, pressure impulse to wave, pressure wave to volume, and volume to light intensity. In fact, there is concreate evidence that shows discrepancy between PRV and HRV, such as that demonstrating the presence of PRV in the absence of HRV, differences in PRV with measurement sites, and differing effects of body posture and exercise between them. Our observations in adult patients with an implanted cardiac pacemaker also indicate that fluctuations in R-R intervals, pulse transit time, and pulse intervals are modulated differently by autonomic functions, respiration, and other factors. The authors suggest that it is more appropriate to recognize PRV as a different biomarker than HRV. Although HRV is a major determinant of PRV, PRV is caused by many other sources of variability, which could contain useful biomedical information that is neither error nor noise.
Topics: Aged, 80 and over; Biomarkers; Female; Heart Rate; Humans; Photoplethysmography; Posture; Signal Processing, Computer-Assisted
PubMed: 32811571
DOI: 10.1186/s40101-020-00233-x -
Sensors (Basel, Switzerland) Jan 2023Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood...
Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky-Golay filter. Two approaches-gradient and local maximum methods-were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal's frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated.
Topics: Humans; Smartphone; Heart Rate; Signal Processing, Computer-Assisted; Oximetry; Oxygen
PubMed: 36679533
DOI: 10.3390/s23020737 -
Tidsskrift For Den Norske Laegeforening... Jun 2012The resting pulse rate appears to be an independent cardiovascular risk factor. The paper reviews the scientific evidence in support of this assertion and discusses how... (Review)
Review
BACKGROUND
The resting pulse rate appears to be an independent cardiovascular risk factor. The paper reviews the scientific evidence in support of this assertion and discusses how the findings of this simple examination may be put to clinical use.
METHOD
We have evaluated the relationship between resting pulse rate, cardiovascular disease and mortality based on evidence retrieved by a search in the Medline database.
RESULTS
The resting pulse rate varies with physical fitness, and high intensity training can decrease the resting pulse. A high resting pulse rate is associated with an elevated risk of cardiovascular disease, and a poorer prognosis in established cardiovascular disease. The relationship between a high resting pulse and death from cardiovascular disease can be explained by well-known pathophysiological mechanisms, but more evidence is needed. In particular, we do not know why the associations between pulse rate and health are weaker in females. Physical exercise is beneficial in prevention and often also in the treatment of cardiovascular disease. We do not yet know how much of the beneficial effects of exercise are mediated through a lowered resting pulse.
INTERPRETATION
Taking the resting pulse should form part of prophylactic health monitoring procedures the same way as the monitoring of other cardiovascular risk markers such as blood pressure, lipids, smoking status and weight. Among patients with established cardiovascular disease, the resting pulse rate is an important prognostic marker. An elevated resting pulse rate might be an incitement to recommend increased physical activity.
Topics: Animals; Cardiovascular Diseases; Exercise; Female; Heart Rate; Humans; Hypertension; Longevity; Male; Physical Endurance; Physical Fitness; Prognosis; Rest; Risk Factors; Sex Factors
PubMed: 22717860
DOI: 10.4045/tidsskr.11.0629 -
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 -
British Heart Journal Feb 1978In a single-blind, randomised, crossover study in 10 asthmatic patients, the effects of approximately equipotent oral doses of 3 cardioselective beta-blockers-atenolol... (Clinical Trial)
Clinical Trial Comparative Study Randomized Controlled Trial
In a single-blind, randomised, crossover study in 10 asthmatic patients, the effects of approximately equipotent oral doses of 3 cardioselective beta-blockers-atenolol (100 mg), metoprolol (100 mg), and acebutolol (300 mg)-and 4 non-cardioselective beta-blockers-proranolol (100 mg), oxprenolol (100 mg), pindolol (5 mg), and timolol (10 mg) upon FEV1 were compared. All drugs, except pindolol, produced a significant reduction in standing pulse rate and prevented an increase in heart rate after inhaled isoprenaline (1500 microgram). All drugs caused a fall in FEV1 but only atenolol did not differ significantly from placebo in this respect. The bronchodilator response to inhaled isoprenaline was blocked by the 4 non-cardioselective drugs; the 3 cardioselective agents permitted some bronchodilatation, but only atenolol did not differ from placebo.
Topics: Adolescent; Adrenergic beta-Antagonists; Adult; Aged; Asthma; Clinical Trials as Topic; Female; Forced Expiratory Volume; Heart Rate; Humans; Isoproterenol; Male; Middle Aged; Respiration
PubMed: 25075
DOI: 10.1136/hrt.40.2.184 -
Progress in Neurobiology Oct 2023The relevance of interactions between autonomic and central nervous systems remains unclear for human brain function and health, particularly when both systems are...
The relevance of interactions between autonomic and central nervous systems remains unclear for human brain function and health, particularly when both systems are challenged under sleep deprivation (SD). We measured brain activity (with fMRI), pulse and respiratory signals, and baseline brain amyloid beta burden (with PET) in healthy participants. We found that SD relative to rested wakefulness (RW) resulted in a significant increase in synchronized low frequency (LF, < 0.1 Hz) activity in an autonomically-related network (AN), including dorsal attention, visual, and sensorimotor regions, which we previously found to have consistent temporal coupling with LF pulse signal changes (regulated by sympathetic tone). SD resulted in a significant phase coherence between the LF component of the pulse signal and a medial network with peak effects in the midbrain reticular formation, and between LF component of the respiratory variations (regulated by respiratory motor output) and a cerebellar network. The LF power of AN during SD was significantly and independently correlated with pulse-medial network and respiratory-cerebellar network phase coherences (total adjusted R = 0.78). Higher LF power of AN during SD (but not RW) was associated with lower amyloid beta burden (Cohen's d = 0.8). In sum, SD triggered an autonomic mode of synchronized brain activity that was associated with distinct autonomic-central interactions. Findings highlight the direct relevance of global cortical synchronization to brain clearance mechanisms.
Topics: Humans; Amyloid beta-Peptides; Autonomic Nervous System; Brain; Heart Rate; Nervous System Physiological Phenomena
PubMed: 37516341
DOI: 10.1016/j.pneurobio.2023.102510 -
Sensors (Basel, Switzerland) Mar 2024Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving...
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.
Topics: Female; Humans; Heart Rate; Autonomic Nervous System; Benchmarking; Correlation of Data; Electrocardiography
PubMed: 38610260
DOI: 10.3390/s24072048 -
Biosensors Oct 2016To achieve sensitivity, comfort, and durability in vital sign monitoring, this study explores the use of radar technologies in wearable devices. The study first detected... (Review)
Review
To achieve sensitivity, comfort, and durability in vital sign monitoring, this study explores the use of radar technologies in wearable devices. The study first detected the respiratory rates and heart rates of a subject at a one-meter distance using a self-injection-locked (SIL) radar and a conventional continuous-wave (CW) radar to compare the sensitivity versus power consumption between the two radars. Then, a pulse rate monitor was constructed based on a bistatic SIL radar architecture. This monitor uses an active antenna that is composed of a SIL oscillator (SILO) and a patch antenna. When attached to a band worn on the subject's wrist, the active antenna can monitor the pulse on the subject's wrist by modulating the SILO with the associated Doppler signal. Subsequently, the SILO's output signal is received and demodulated by a remote frequency discriminator to obtain the pulse rate information.
Topics: Heart Rate; Humans; Monitoring, Physiologic; Pulse; Radar; Vital Signs; Wrist
PubMed: 27792176
DOI: 10.3390/bios6040054 -
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