-
Critical Care Medicine Mar 2021The presence of tachycardia in critically ill patients is frequently used as an indication of severity of illness and to guide treatment decisions but can be influenced...
OBJECTIVE
The presence of tachycardia in critically ill patients is frequently used as an indication of severity of illness and to guide treatment decisions but can be influenced by body temperature, thus confounding its interpretation. There are few data available on the relationship between body temperature and heart rate in critically ill patients.
DESIGN
Retrospective analysis of prospectively collected data.
SETTING
Mixed medical-surgical university hospital ICU.
PATIENTS
All patients admitted to the ICU between November 2006 and August 2019.
MEASUREMENTS AND MAIN RESULTS
Body temperature was recorded in the electronic medical records at least hourly, from invasive measurements (esophageal probe, indwelling urinary catheter, pulse contour cardiac output monitoring system, or pulmonary artery catheter) or manual tympanic recordings. Heart rate was monitored continuously and hourly values were recorded in the electronic medical record. Change in heart rate with change in body temperature was assessed by extracting pairs of simultaneous body temperature and corresponding heart rate measurements from the electronic medical record: 472,941 simultaneous pairs were obtained from the 9,046 patients admitted during the study period. Each 1°C increase in body temperature between 32.0°C and 42.0°C was associated with an 8.35 beats/min increase in heart rate. Crude linear regression showed an r2 of 0.855 between body temperature and heart rate. Heart rate increased more in females than in males (9.46 vs 7.24 beats/min for each 1°C, p < 0.0001); this relationship was not affected by age or adrenergic drugs. The increase in heart rate was related to the severity of organ dysfunction.
CONCLUSIONS
Increase in body temperature is associated with a linear increase in heart rate of 9.46 beats/min/°C in female and 7.24 beats/min/°C in male patients. These observations will help to correctly interpret heart rate values at different body temperatures and enable more accurate evaluation of other factors associated with tachycardia.
Topics: Adult; Body Temperature; Critical Care; Critical Illness; Female; Heart Rate; Humans; Intensive Care Units; Male; Middle Aged; Monitoring, Physiologic; Retrospective Studies
PubMed: 33566464
DOI: 10.1097/CCM.0000000000004807 -
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 -
Current Hypertension Reports Jan 2020Aortic stiffness (AS) is widely associated with hypertension and considered as a major predictor of coronary heart disease (CHD). AS is measured using carotid-femoral... (Review)
Review
PURPOSE OF REVIEW
Aortic stiffness (AS) is widely associated with hypertension and considered as a major predictor of coronary heart disease (CHD). AS is measured using carotid-femoral pulse wave velocity (PWV), particularly when this parameter is associated with an index involving age, gender, heart rate, and mean blood pressure. The present review focuses on the interest of measurement of PWV and the calculation of individual PWV index for the prediction of CHD, in addition with the use of new statistical nonlinear models enabling results with very high levels of accuracy.
RECENT FINDINGS
PWV index may so constitute a substantial marker of large arteries prediction and damage in CHD and may be also used in cerebrovascular and renal circulations models. PWV index determinations are particularly relevant to consider in angiographic CHD decisions and in the presence of vulnerable plaques with high cardiovascular risk. Due to the variability in symptoms and clinical characteristics of patients, together with some imperfections in results, there is no very simple adequate diagnosis approach enabling to improve the so defined CHD prediction in usual clinical practice. In recent works in relation to "artificial intelligence" and involving "decision tree" models and "artificial neural networks," it has been possible to determine consistent pathways introducing predictive medicine and enabling to obtain efficient algorithm classification models of coronary prediction.
Topics: Coronary Disease; Heart Rate; Humans; Hypertension; Pulse Wave Analysis; Vascular Stiffness
PubMed: 31925555
DOI: 10.1007/s11906-019-1006-z -
Computer Methods and Programs in... May 2022Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the...
Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography.
OBJECTIVE
Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content.
METHODS
PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests.
RESULTS
From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz.
CONCLUSION
The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application.
SIGNIFICANCE
The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.
Topics: Algorithms; Electrocardiography; Heart Rate; Humans; Photoplethysmography; Reproducibility of Results; Signal Processing, Computer-Assisted; Syndactyly
PubMed: 35255373
DOI: 10.1016/j.cmpb.2022.106724 -
Medical & Biological Engineering &... Oct 2023Remote photoplethysmography (rPPG) enables contact-free monitoring of the pulse rate by using a color camera. The fundamental limitation is that motion artifacts and...
Remote photoplethysmography (rPPG) enables contact-free monitoring of the pulse rate by using a color camera. The fundamental limitation is that motion artifacts and changes in ambient light conditions greatly affect the accuracy of pulse-rate monitoring. We propose use of a high-speed camera and a motion suppression algorithm with high computational efficiency. This system incorporates a number of major improvements including reproduction of pulse wave details, high-precision pulse-rate monitoring of moving subjects, and excellent scene scalability. A series of quantization methods were used to evaluate the effect of different frame rates and different algorithms in pulse-rate monitoring of moving subjects. The experimental results show that use of 180-fps video and a Plane-Orthogonal-to-Skin (POS) algorithm can produce high-precision pulse-rate monitoring results with mean absolute error can be less than 5 bpm and the relative accuracy reaching 94.5%. Thus, it has significant potential to improve personal health care and intelligent health monitoring.
Topics: Humans; Heart Rate; Pulse; Skin; Photoplethysmography; Motion; Algorithms; Signal Processing, Computer-Assisted
PubMed: 37474842
DOI: 10.1007/s11517-023-02884-1 -
Annual International Conference of the... Nov 2021The COVID-19 pandemic is a global health crisis. Mental health is critical in such uncertain situations, particularly when people are required to significantly restrict...
The COVID-19 pandemic is a global health crisis. Mental health is critical in such uncertain situations, particularly when people are required to significantly restrict their movements and change their lifestyles. Under these conditions, many countries have turned to telemedicine to strengthen and expand mental health services. Our research group previously developed a mental illness screening system based on heart rate variability (HRV) analysis, enabling an objective and easy mental health self-check. This screening system cannot be used for telemedicine because it uses electrocardiography (ECG) and contact photoplethysmography (PPG), that are not widely available outside of a clinical setting. The purpose of this study is to enable the extension of the aforementioned system to telemedicine by the application of non-contact PPG using an RGB webcam, also called imaging- photoplethysmography (iPPG). The iPPG measurement errors occur due to changes in the relative position between the camera and the target, and due to changes in light. Conventionally, in image processing, the pixel value of the entire face region is used. We propose skin pixel extraction to eliminate blinks, eye movements, and changes in light and shadow. In signal processing, the green channel signal is conventionally used as a pulse wave owing to the absorption characteristics of blood flow. Taking advantage of the fact that the red and blue channels contain noise, we propose a signal reconstruction method for removing noise and strengthening the signal in the pulse rate variability (PRV) frequency band by weighting the three signals of the RGB camera. We conducted an experiment with 13 healthy subjects, and showed that the PRV index and pulse rate (PR) errors estimated by the proposed method were smaller than those of the conventional method. The correlation coefficients between estimated values by the proposed method and reference values of LF, HF, and PR were 0.86, 0.69, and 0.96, respectively.
Topics: COVID-19; Heart Rate; Humans; Mental Disorders; Pandemics; SARS-CoV-2
PubMed: 34892718
DOI: 10.1109/EMBC46164.2021.9630038 -
Annual International Conference of the... Jul 2020Photoplethysmography (PPG) is a non-invasive, low-cost optical technique used to assess the cardiovascular system. In recent years, PPG-based heart rate measurement has...
Photoplethysmography (PPG) is a non-invasive, low-cost optical technique used to assess the cardiovascular system. In recent years, PPG-based heart rate measurement has gained significant attention due to its popularity in wearable devices, as well as its practicality relative to electrocardiography (ECG). Studies comparing the dynamics of ECG- and PPG-based heart rate measures have found small differences between these two modalities; differences related to the physiological processes behind each technique. In this work, we analyzed the spectral coherence and the signal-to-noise ratio between isolated PPG pulses and the raw PPG signal in order to: (i) determine the optimal filter to enhance pulse detection from raw PPG for improved heart rate estimation, and (ii) characterize the spectral content of the PPG pulse. The proposed methods were evaluated on 27000 pulses from a PPG database acquired from 42 participants (adults and children). The results showed that the optimal bandpass filter to enhance PPG from the adult group was 0.6-3.3 Hz, while for the children group it was 1.0-2.7 Hz. The spectral analysis on the pulse signal showed that similar bandwidths were found for the adult (0.8-2.4 Hz) and children (0.9-2.7 Hz) groups. We hope that the results presented herein serve as a baseline for pulse detection algorithms and assist with the development of more sophisticated PPG processing algorithms.
Topics: Adult; Algorithms; Child; Electrocardiography; Heart Rate; Humans; Photoplethysmography; Signal Processing, Computer-Assisted
PubMed: 33018133
DOI: 10.1109/EMBC44109.2020.9175396 -
Journal of the Association For Research... Jun 2023The auditory brainstem implant (ABI) is an auditory neuroprosthesis that provides hearing by electrically stimulating the cochlear nucleus (CN) of the brainstem. Our...
The auditory brainstem implant (ABI) is an auditory neuroprosthesis that provides hearing by electrically stimulating the cochlear nucleus (CN) of the brainstem. Our previous study (McInturff et al., 2022) showed that single-pulse stimulation of the dorsal (D)CN subdivision with low levels of current evokes responses that have early latencies, different than the late response patterns observed from stimulation of the ventral (V)CN. How these differing responses encode more complex stimuli, such as pulse trains and amplitude modulated (AM) pulses, has not been explored. Here, we compare responses to pulse train stimulation of the DCN and VCN, and show that VCN responses, measured in the inferior colliculus (IC), have less adaption, higher synchrony, and higher cross-correlation. However, with high-level DCN stimulation, responses become like those to VCN stimulation, supporting our earlier hypothesis that current spreads from electrodes on the DCN to excite neurons located in the VCN. To AM pulses, stimulation of the VCN elicits responses with larger vector strengths and gain values especially in the high-CF portion of the IC. Additional analysis using neural measures of modulation thresholds indicate that these measures are lowest for VCN. Human ABI users with low modulation thresholds, who score best on comprehension tests, may thus have electrode arrays that stimulate the VCN. Overall, the results show that the VCN has superior response characteristics and suggest that it should be the preferred target for ABI electrode arrays in humans.
Topics: Animals; Humans; Auditory Brain Stem Implants; Heart Rate; Cochlear Nucleus; Hearing; Models, Animal; Electric Stimulation
PubMed: 37156973
DOI: 10.1007/s10162-023-00897-z