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Acta Paediatrica (Oslo, Norway : 1992) Jun 2022Heart rate (HR) is the most important parameter to evaluate newborns' clinical condition and to guide intervention during resuscitation at birth. The present study aims...
AIM
Heart rate (HR) is the most important parameter to evaluate newborns' clinical condition and to guide intervention during resuscitation at birth. The present study aims to compare the accuracy of NeoBeat dry-electrode ECG for HR measurement with conventional ECG and pulse oximetry (PO).
METHODS
Newborns with a gestational age ≥32 weeks and/or birth weight ≥1.5 kg were included when HR evaluation was needed. HR was simultaneously measured for 10 min with NeoBeat, PO and conventional ECG.
RESULTS
A total of 18 infants were included (median (IQR) gestational age 39 (36-39) weeks and birth weight 3 150 (2 288-3 859) grams). Mean (SD) duration until NeoBeat obtained a reliable signal was 2.5 (9.0) s versus 58.5 (171.0) s for PO. Mean difference between NeoBeat and ECG was 1.74 bpm (LoA -4.987-8.459 and correlation coefficient 0.98). Paired HR measurements over 30-s intervals revealed no significant difference between NeoBeat and ECG. The positive predictive value of a detected HR <100 bpm by NeoBeat compared with ECG was 54.84%, negative predictive value 99.99%, sensitivity 94.44%, specificity 99.99% and accuracy 99.85%.
CONCLUSIONS
HR measurement with NeoBeat dry-electrode ECG at birth is reliable and accurate.
Topics: Adult; Birth Weight; Electrocardiography; Electrodes; Female; Heart Rate; Humans; Infant; Infant, Newborn; Oximetry
PubMed: 34981852
DOI: 10.1111/apa.16242 -
Minerva Medica Oct 2022Isolated systolic hypertension in the young (ISHY) remains a challenging problem, partly due to the differences in central aortic pressure observed in studies... (Review)
Review
Isolated systolic hypertension in the young (ISHY) remains a challenging problem, partly due to the differences in central aortic pressure observed in studies investigating ISHY. The fundamental relationship between heart rate and central aortic pressure, and more precisely, the relationship between heart rate and amplification of central aortic pressure in the periphery, underpins the assessment and, as a consequence, the treatment of ISHY. Physiology warrants that an increase in heart rate would lead to increased amplification of the pressure pulse between the aorta and the brachial artery. Heart rate generally decreases with age, in particular over the first two decades of life. Thus, a higher heart rate in the young would result in higher pulse pressure amplification, and therefore an elevated brachial systolic pressure would not necessarily translate to elevated aortic systolic pressure. However, elevated heart rate is not a consistent feature in ISHY, and studies have shown that ISHY can present with either high or low central aortic systolic pressure. In this brief review, we summarize the physiological aspects underlying the relationship between heart rate and central aortic blood pressure and its amplification in the brachial artery, how this relationship changes with age, and examine the implications of these effects on the assessment and treatment of ISHY.
Topics: Humans; Arterial Pressure; Heart Rate; Isolated Systolic Hypertension
PubMed: 34333956
DOI: 10.23736/S0026-4806.21.07631-X -
Psychosomatic Medicine Sep 2023Heart rate is a transdiagnostic correlate of affective states and the stress diathesis model of health. Although most psychophysiological research has been conducted in...
OBJECTIVE
Heart rate is a transdiagnostic correlate of affective states and the stress diathesis model of health. Although most psychophysiological research has been conducted in laboratory environments, recent technological advances have provided the opportunity to index pulse rate dynamics in real-world environments with commercially available mobile health and wearable photoplethysmography (PPG) sensors that allow for improved ecologically validity of psychophysiological research. Unfortunately, adoption of wearable devices is unevenly distributed across important demographic characteristics, including socioeconomic status, education, and age, making it difficult to collect pulse rate dynamics in diverse populations. Therefore, there is a need to democratize mobile health PPG research by harnessing more widely adopted smartphone-based PPG to both promote inclusivity and examine whether smartphone-based PPG can predict concurrent affective states.
METHODS
In the current preregistered study with open data and code, we examined the covariation of smartphone-based PPG and self-reported stress and anxiety during an online variant of the Trier Social Stress Test, as well as prospective relationships between PPG and future perceptions of stress and anxiety in a sample of 102 university students.
RESULTS
Smartphone-based PPG significantly covaries with self-reported stress and anxiety during acute digital social stressors. PPG pulse rate was significantly associated with concurrent self-reported stress and anxiety ( b = 0.44, p = .018) as well as prospective stress and anxiety at the subsequent time points, although the strength of this association diminished the farther away pulse rate got from self-reported stress and anxiety (lag 1 model: b = 0.42, p = .024; lag 2 model: b = 0.38, p = .044).
CONCLUSIONS
These findings indicate that PPG provides a proximal measure of the physiological correlates of stress and anxiety. Smartphone-based PPG can be used as an inclusive method for diverse populations to index pulse rate in remote digital study designs.
Topics: Humans; Heart Rate; Photoplethysmography; Smartphone; Prospective Studies; Anxiety
PubMed: 37409791
DOI: 10.1097/PSY.0000000000001178 -
Biomedical Engineering Online Jan 2018In the last few years, some studies have measured heart rate (HR) or heart rate variability (HRV) parameters using a video camera. This technique focuses on the...
BACKGROUND
In the last few years, some studies have measured heart rate (HR) or heart rate variability (HRV) parameters using a video camera. This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. To date, most of these works have obtained HRV parameters in stationary conditions, and there are practically no studies that obtain these parameters in motion scenarios and by conducting an in-depth statistical analysis.
METHODS
In this study, a video pulse rate variability (PRV) analysis is conducted by measuring the pulse-to-pulse (PP) intervals in stationary and motion conditions. Firstly, given the importance of the sampling rate in a PRV analysis and the low frame rate of commercial cameras, we carried out an analysis of two models to evaluate their performance in the measurements. We propose a selective tracking method using the Viola-Jones and KLT algorithms, with the aim of carrying out a robust video PRV analysis in stationary and motion conditions. Data and results of the proposed method are contrasted with those reported in the state of the art.
RESULTS
The webcam achieved better results in the performance analysis of video cameras. In stationary conditions, high correlation values were obtained in PRV parameters with results above 0.9. The PP time series achieved an RMSE (mean ± standard deviation) of 19.45 ± 5.52 ms (1.70 ± 0.75 bpm). In the motion analysis, most of the PRV parameters also achieved good correlation results, but with lower values as regards stationary conditions. The PP time series presented an RMSE of 21.56 ± 6.41 ms (1.79 ± 0.63 bpm).
CONCLUSIONS
The statistical analysis showed good agreement between the reference system and the proposed method. In stationary conditions, the results of PRV parameters were improved by our method in comparison with data reported in related works. An overall comparative analysis of PRV parameters in motion conditions was more limited due to the lack of studies or studies containing insufficient data analysis. Based on the results, the proposed method could provide a low-cost, contactless and reliable alternative for measuring HR or PRV parameters in non-clinical environments.
Topics: Adult; Algorithms; Body Mass Index; Equipment Design; Female; Heart Rate; Humans; Image Processing, Computer-Assisted; Male; Models, Theoretical; Motion; Photoplethysmography; Pulse; Signal Processing, Computer-Assisted; Video Recording
PubMed: 29378598
DOI: 10.1186/s12938-018-0437-0 -
Applied Psychophysiology and Biofeedback Sep 2022Pulse rate variability is a physiological parameter that has been extensively studied and correlated with many physical ailments. However, the phase relationship between...
Pulse rate variability is a physiological parameter that has been extensively studied and correlated with many physical ailments. However, the phase relationship between inter-beat interval, IBI, and breathing has very rarely been studied. Develop a technique by which the phase relationship between IBI and breathing can be accurately and efficiently extracted from photoplethysmography (PPG) data. A program based on Lock-in Amplifier technology was written in Python to implement a novel technique, Dynamic Phase Extraction. It was tested using a breath pacer and a PPG sensor on 6 subjects who followed a breath pacer at varied breathing rates. The data were then analyzed using both traditional methods and the novel technique (Dynamic Phase Extraction) utilizing a breath pacer. Pulse data was extracted using a PPG sensor. Dynamic Phase Extraction (DPE) gave the magnitudes of the variation in IBI associated with breathing [Formula: see text] measured with photoplethysmography during paced breathing (with premature ventricular contractions, abnormal arrhythmias, and other artifacts edited out). [Formula: see text] correlated well with two standard measures of pulse rate variability: the Standard Deviation of the inter-beat interval (SDNN) (ρ = 0.911) and with the integrated value of the Power Spectral Density between 0.04 and 0.15 Hz (Low Frequency Power or LF Power) (ρ = 0.885). These correlations were comparable to the correlation between the SDNN and the LF Power (ρ = 0.877). In addition to the magnitude [Formula: see text], Dynamic Phase Extraction also gave the phase between the breath pacer and the changes in the inter-beat interval (IBI) due to respiratory sinus arrythmia (RSA), and correlated well with the phase extracted using a Fourier transform (ρ = 0.857). Dynamic Phase Extraction can extract both the phase between the breath pacer and the changes in IBI due to the respiratory sinus arrhythmia component of pulse rate variability ([Formula: see text], but is limited by needing a breath pacer.
Topics: Electrocardiography; Heart Rate; Humans; Photoplethysmography; Respiratory Rate; Respiratory Sinus Arrhythmia; Signal Processing, Computer-Assisted
PubMed: 35704121
DOI: 10.1007/s10484-022-09549-z -
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 -
Hypertension Research : Official... Apr 2023
Topics: Humans; Cardiovascular Diseases; Natriuretic Peptide, Brain; Heart Rate; Risk Factors; Hypertension; Blood Pressure; Heart Disease Risk Factors
PubMed: 36697875
DOI: 10.1038/s41440-023-01186-1 -
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 -
Methods of Information in Medicine May 2016Heart rate variability (HRV) is a signal obtained from RR intervals of electrocardiography (ECG) signals to evaluate the balance between the sympathetic nervous system...
BACKGROUND
Heart rate variability (HRV) is a signal obtained from RR intervals of electrocardiography (ECG) signals to evaluate the balance between the sympathetic nervous system and the parasympathetic nervous system; not only HRV but also pulse rate variability (PRV) extracted from finger pulse plethysmography (PPG) can reflect irregularities that may occur in heart rate and control procedures.
OBJECTIVES
The purpose of this study is to compare the HRV and PRV during hypoglycemia in order to evaluate the features that computed from PRV that can be used in detection of hypoglycemia.
METHODS
To this end, PRV and HRV of 10 patients who required testing with insulin-induced hypoglycemia (IIHT) in Clinics of Endocrinology and Metabolism Diseases of Bezm-i Alem University (Istanbul, Turkey), were obtained. The recordings were done at three stages: prior to IIHT, during the IIHT, and after the IIHT. We used Bland-Altman analysis for comparing the parameters and to evaluate the correlation between HRV and PRV if exists.
RESULTS
Significant correlation (r > 0.90, p < 0.05) and close agreement were found between HRV and PRV for mean intervals, the root-mean square of the difference of successive intervals, standard deviation of successive intervals and the ratio of the low-to-high frequency power.
CONCLUSIONS
In conclusion, all the features computed from PRV and HRV have close agreement and correlation according to Bland-Altman analyses' results and features computed from PRV can be used in detection of hypoglycemia.
Topics: Algorithms; Female; Heart Rate; Humans; Hypoglycemia; Insulin; Male; Middle Aged; Pulse; Signal Processing, Computer-Assisted
PubMed: 27063926
DOI: 10.3414/ME15-01-0088 -
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