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PloS One 2022Vital signs follow circadian patterns in both healthy volunteers and critically ill patients, which seem to be influenced by disease severity in the latter. In this...
RATIONALE
Vital signs follow circadian patterns in both healthy volunteers and critically ill patients, which seem to be influenced by disease severity in the latter. In this study we explored the existence of circadian patterns in heart rate, respiratory rate and skin temperature of hospitalized COVID-19 patients, and aimed to explore differences in circadian rhythm amplitude during patient deterioration.
METHODS
We performed a retrospective study of COVID-19 patients admitted to the general ward of a tertiary hospital between April 2020 and March 2021. Patients were continuously monitored using a wireless sensor and fingertip pulse oximeter. Data was divided into three cohorts: patients who recovered, patients who developed respiratory insufficiency and patients who died. For each cohort, a population mean cosinor model was fitted to detect rhythmicity. To assess changes in amplitude, a mixed-effect cosinor model was fitted.
RESULTS
A total of 429 patients were monitored. Rhythmicity was observed in heartrate for the recovery cohort (p<0.001), respiratory insufficiency cohort (p<0.001 and mortality cohort (p = 0.002). Respiratory rate showed rhythmicity in the recovery cohort (p<0.001), but not in the other cohorts (p = 0.18 and p = 0.51). Skin temperature also showed rhythmicity in the recovery cohort (p<0.001), but not in the other cohorts (p = 0.22 and p = 0.12). For respiratory insufficiency, only the amplitude of heart rate circadian pattern increased slightly the day before (1.2 (99%CI 0.16-2.2, p = 0.002)). In the mortality cohort, the amplitude of heart rate decreased (-1.5 (99%CI -2.6- -0.42, p<0.001)) and respiratory rate amplitude increased (0.72 (99%CI 0.27-1.3, p = 0.002) the days before death.
CONCLUSION
A circadian rhythm is present in heart rate of COVID-19 patients admitted to the general ward. For respiratory rate and skin temperature, rhythmicity was only found in patients who recover, but not in patients developing respiratory insufficiency or death. We found no consistent changes in circadian rhythm amplitude accompanying patient deterioration.
Topics: COVID-19; Circadian Rhythm; Heart Rate; Humans; Respiratory Insufficiency; Respiratory Rate; Retrospective Studies; Skin Temperature
PubMed: 35797369
DOI: 10.1371/journal.pone.0268065 -
Ugeskrift For Laeger Feb 2019This review summarises the knowledge of pain monitoring during surgery in Denmark. General anaesthesia consists of hypnosis, relaxation and analgesia. The first two can... (Review)
Review
This review summarises the knowledge of pain monitoring during surgery in Denmark. General anaesthesia consists of hypnosis, relaxation and analgesia. The first two can be objectively monitored, but analgesia is traditionally evalu-ated by the anaesthesiologist. Several monitors for as-sessing pain exist, and all types of pain monitoring are superior to traditional evaluation and seem to have several advantages but are still not used routinely in Denmark. The nociception level monitor is multimodal and incorporates movement, heart rate, heart rate variability, pulse plethysmography, skin temperature and galvanic skin response. It has been validated, but further research is needed.
Topics: Analgesia; Anesthesia, General; Denmark; Heart Rate; Humans; Pain; Pain Measurement
PubMed: 30821246
DOI: No ID Found -
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 -
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 -
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 -
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 -
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 -
American Journal of Respiratory and... Sep 2022
Topics: Adult; Cardiovascular Diseases; Continuous Positive Airway Pressure; Coronary Artery Disease; Disorders of Excessive Somnolence; Heart Disease Risk Factors; Heart Rate; Humans; Risk Factors; Sleep Apnea, Obstructive; Sleepiness
PubMed: 35675563
DOI: 10.1164/rccm.202206-1050ED -
Noninvasive Non-Contact SpO Monitoring Using an Integrated Polarization-Sensing CMOS Imaging Sensor.Sensors (Basel, Switzerland) Oct 2022In the diagnosis and primary health care of an individual, estimation of the pulse rate and blood oxygen saturation (SpO2) is critical. The pulse rate and SpO2 are...
BACKGROUND
In the diagnosis and primary health care of an individual, estimation of the pulse rate and blood oxygen saturation (SpO2) is critical. The pulse rate and SpO2 are determined by methods including photoplethysmography (iPPG), light spectroscopy, and pulse oximetry. These devices need to be compact, non-contact, and noninvasive for real-time health monitoring. Reflection-based iPPG is becoming popular as it allows non-contact estimation of the heart rate and SpO2. Most iPPG methods capture temporal data and form complex computations, and thus real-time measurements and spatial visualization are difficult.
METHOD
In this research work, reflective mode polarized imaging-based iPPG is proposed. For polarization imaging, a custom image sensor with wire grid polarizers on each pixel is designed. Each pixel has a wire grid of varying transmission axes, allowing phase detection of the incoming light. The phase information of the backscattered light from the fingertips of 12 healthy volunteers was recorded in both the resting as well as the excited states. These data were then processed using MATLAB 2021b software.
RESULTS
The phase information provides quantitative information on the reflection from the superficial and deep layers of skin. The ratio of deep to superficial layer backscattered phase information is shown to be directly correlated and linearly increasing with an increase in the SpO2 and heart rate.
CONCLUSIONS
The phase-based measurements help to monitor the changes in the resting and excited state heart rate and SpO2 in real time. Furthermore, the use of the ratio of phase information helps to make the measurements independent of the individual skin traits and thus increases the accuracy of the measurements. The proposed iPPG works in ambient light, relaxing the instrumentation requirement and helping the system to be compact and portable.
Topics: Humans; Oximetry; Photoplethysmography; Monitoring, Physiologic; Heart Rate; Fingers; Oxygen
PubMed: 36298147
DOI: 10.3390/s22207796 -
Schizophrenia Bulletin Jan 2022Patients with psychiatric disorders have an increased risk of cardiovascular pathologies. A bidirectional feedback model between the brain and heart exists widely in... (Comparative Study)
Comparative Study
OBJECTIVES
Patients with psychiatric disorders have an increased risk of cardiovascular pathologies. A bidirectional feedback model between the brain and heart exists widely in both psychotic and nonpsychotic disorders. The aim of this study was to compare heart rate variability (HRV) and pulse wave velocity (PWV) functions between patients with psychotic and nonpsychotic disorders and to investigate whether subgroups defined by HRV and PWV features improve the transdiagnostic psychopathology of psychiatric classification.
METHODS
In total, 3448 consecutive patients who visited psychiatric or psychological health services with psychotic (N = 1839) and nonpsychotic disorders (N = 1609) and were drug-free for at least 2 weeks were selected. HRV and PWV indicators were measured via finger photoplethysmography during a 5-minute period of rest. Canonical variates were generated through HRV and PWV indicators by canonical correlation analysis (CCA).
RESULTS
All HRV indicators but none of the PWV indicators were significantly reduced in the psychotic group relative to those in the nonpsychotic group. After adjusting for age, gender, and body mass index, many indices of HRV were significantly reduced in the psychotic group compared with those in the nonpsychotic group. CCA analysis revealed 2 subgroups defined by distinct and relatively homogeneous patterns along HRV and PWV dimensions and comprising 19.0% (subgroup 1, n = 655) and 80.9% (subgroup 2, n = 2781) of the sample, each with distinctive features of HRV and PWV functions.
CONCLUSIONS
HRV functions are significantly impaired among psychiatric patients, especially in those with psychosis. Our results highlight important subgroups of psychiatric patients that have distinct features of HRV and PWV which transcend current diagnostic boundaries.
Topics: Adult; Autonomic Nervous System; Cardiovascular Diseases; Comorbidity; Female; Heart Rate; Humans; Male; Mental Disorders; Middle Aged; Plethysmography; Psychotic Disorders; Pulse Wave Analysis
PubMed: 34313787
DOI: 10.1093/schbul/sbab080