Pulse Rate Measurements Research Articles

Comparison of the Effectiveness of Two Different Methods of Alleviating Pain and Anxiety during Local Anaesthesia Administration in Children: A Split-mouth Study

Abstract Introduction: This study aimed to compare the effectiveness of two different methods, a custom-designed mucosal vibrator and topical benzocaine gel, in alleviating pain and anxiety during local anaesthesia (LA) administration in paediatric patients. Materials and Methods: A randomised, split-mouth controlled trial was conducted with 30 paediatric patients aged 5–10 years. Each child received LA with a custom-designed mucosal vibrator in one session and with topical benzocaine gel in another. Pain perception and anxiety were assessed using the Wong-Baker FACES Pain Rating Scale, Sound–Eye–Motor Scale and pulse rate measurements. The sequence of interventions was randomised using the sequentially numbered, opaque, sealed envelope technique. Results: The study found that the custom-designed mucosal vibrator significantly reduced pain perception compared to the benzocaine gel, as evidenced by both subjective and objective pain scores (P < 0.05). However, the two methods showed no significant difference in anxiety levels, as indicated by similar pulse rate changes (P > 0.05). Conclusion: The custom-designed mucosal vibrator offers a cost-effective and efficient alternative to topical benzocaine for reducing pain during LA in children, though it does not significantly alter anxiety levels. This device could be a valuable tool for paediatric dental practitioners to improve the comfort of their patients during dental procedures.

Accuracy of remote, video-based supraventricular tachycardia detection in patients undergoing elective electrical cardioversion: a prospective cohort.

Unobtrusive pulse rate monitoring by continuous video recording, based on remote photoplethysmography (rPPG), might enable early detection of perioperative arrhythmias in general ward patients. However, the accuracy of an rPPG-based machine learning model to monitor the pulse rate during sinus rhythm and arrhythmias is unknown. We conducted a prospective, observational diagnostic study in a cohort with a high prevalence of arrhythmias (patients undergoing elective electrical cardioversion). Pulse rate was assessed with rPPG via a visible light camera and ECG as reference, before and after cardioversion. A cardiologist categorized ECGs into normal sinus rhythm or arrhythmias requiring further investigation. A supervised machine learning model (support vector machine with Gaussian kernel) was trained using rPPG signal features from 60-s intervals and validated via leave-one-subject-out. Pulse rate measurement performance was evaluated with Bland-Altman analysis. Of 72 patients screened, 51 patients were included in the analyses, including 444 60-s intervals with normal sinus rhythm and 1130 60-s intervals of clinically relevant arrhythmias. The model showed robust discrimination (AUC 0.95 [0.93-0.96]) and good calibration. For pulse rate measurement, the bias and limits of agreement for sinus rhythm were 1.21 [- 8.60 to 11.02], while for arrhythmia, they were - 7.45 [- 35.75 to 20.86]. The machine learning model accurately identified sinus rhythm and arrhythmias using rPPG in real-world conditions. Heart rate underestimation during arrhythmias highlights the need for optimization.

Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis.

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models.

LOTUS Software to Process Wearable EmbracePlus Data.

The Empatica EmbracePlus is a recent innovation in medical-grade wristband wearable sensors that enable unobtrusive continuous measurement of pulse rate, electrodermal activity, skin temperature, and various accelerometry-based actigraphy measures using a minimalistic smartwatch design. The advantage of this lightweight wearable is the potential for holistic longitudinal recording and monitoring of physiological processes that index a suite of autonomic functions, as well as to provide ecologically valid insights into human behaviour, health, physical activity, and psychophysiological processes. Given the longitudinal nature of wearable recordings, EmbracePlus data collection is managed by storing raw timeseries in short 'chunks' in avro file format organised by universal standard time. This is memory-efficient but requires programming expertise to compile the raw data into continuous file formats that can be processed using standard techniques. Currently, there are no accessible tools available to compile and analyse raw EmbracePlus data over user-defined time periods. To address that, we introduce the LOTUS toolkit, an open-source graphical user interface that allows users to reconstitute and process EmbracePlus datasets over select time intervals. LOTUS is available on GitHub, and currently allows users to compile raw EmbracePlus data into unified timeseries stored in more familiar Excel or Matlab file formats to facilitate signal processing and analysis. Future work will expand the toolkit to process Empatica E4 and other wearable signal data, while also integrating more sophisticated functions for feature extraction and analysis.

Integrating genome-wide information and wearable device data to explore the link of anxiety and antidepressants with pulse rate variability.

This study explores the genetic and epidemiologic correlates of long-term photoplethysmography-derived pulse rate variability (PRV) measurements with anxiety disorders. Individuals with whole-genome sequencing, Fitbit, and electronic health record data (N = 920; 61,333 data points) were selected from the All of Us Research Program. Anxiety polygenic risk scores (PRS) were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts- UK Biobank, FinnGen, and the Million Veterans Program (NTotal =364,550). PRV was estimated as the standard deviation of average five-minute pulse wave intervals over full 24-hour pulse rate measurements (SDANN). Antidepressant exposure was defined as an active antidepressant prescription at the time of the PRV measurement in the EHR. Anxiety PRS and antidepressant use were tested for association with daily SDANN. The potential causal effect of anxiety on PRV was assessed with one-sample Mendelian randomization (MR). Anxiety PRS was independently associated with reduced SDANN (beta = -0.08; p = 0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta = -0.12, p = 0.002) and bupropion (beta = -0.071, p = 0.01), tricyclic antidepressants (beta = -0.177, p = 0.0008), selective serotonin reuptake inhibitors (beta = -0.069; p = 0.0008) and serotonin and norepinephrine reuptake inhibitors (beta = -0.16; p = 2×10-6) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta = -2.22, p = 0.03). Anxiety and antidepressants are independently associated with decreased PRV, and anxiety appears to exert a causal effect on reduced PRV. Those observational findings provide insights into the impact of anxiety on PRV.

Prevalence of Hypertension and Prehypertension among Young Adult Students of Midnapore Town, Paschim Medinipur, West Bengal, India

Background: Hypertension is one of the most prevalent public health concerns and also one of the prominent factors in affecting the cardiovascular diseases (CVDs), coronary heart diseases (CHDs). It has been termed as “Silent Killer’. About 46.0% of adults worldwide with hypertension are unaware that they have the condition. Methods: The present study aimed at the investigation on the anthropometric, derived variables and blood pressure variables. It also determined the correlation between anthropometric, derived variables and blood pressure variables. It estimated the prevalence rate of hypertension and prehypertension among the young adult students of Midnapore Town, Paschim Medinipur, West Bengal, India. The cross-sectional study was conducted among 201 young adult male students aged between 18 to 28 years. Height (HT), Weight (WT), Mid upper arm circumference (MUAC), Hip circumference (HC), Waist circumference (WC), Biceps skinfold (BSF), Triceps skinfold (TSF), Subscapular skinfold (SSF) and Suprailliac skinfold (SISF), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP) and pulse rate measurement were taken using standard procedure. Results: The overall prevalence of hypertension was found to be 10.9% among the studied participants and 44.3% was the prehypertension prevalence rate. A significant correlation (p<0.05) was found between blood pressure variables and different anthropometric and derived variables. Conclusion: This study demonstrated that there is a very strong prevalence of prehypertension and HTN among the young adult students of Midnapore town. Results of such kind of researches can be helpful in the formulation of appropriate awareness programmes in the universities and colleges.

Electroencephalogram of Normotensive Individuals During Sitting and Standing Blood Pressure Measurement Positions.

Background Investigating changes in brain electroencephalogram activity during blood pressure measurement in sitting and standing positions is clinically significant for understanding the neural correlates of postural changes, which may affect cerebral perfusion and autonomic regulation. Shifts in blood pressure can influence visual cortical activity, potentially altering cognitive and sensory processing. This research holds clinical relevance in evaluating disorders like orthostatic hypotension and syncope, where postural changes compromise cerebral blood flow. Furthermore, it can help refine diagnostic protocols and management strategies for neurovascular and autonomic dysfunction. The study also highlighted the possibility of using polygraphs to identify and manage discrepancies in blood pressure measurement caused by posture-induced changes in brain activities for accurate diagnosis. Methodology The electroencephalogram of healthy normotensive female individuals undergoing blood pressure measurements at sitting and standing positions was investigated. Ten healthy young adult females who satisfy the inclusion criteria were selected. Electroencephalographic (EEG) waves were recorded with the aid of Powerlab 26T (ADInstruments Pty Ltd., Bella Vista, Australia). Blood pressure and pulse rate measurements were conducted using a sphygmomanometer and stopwatch. Results During sitting and standing blood pressure positions, diastolic blood pressure and pulse rate were higher when compared to baseline values but the standing blood pressure position showed a higher pulse rate than the sitting blood pressure position. Although alpha wave frequency was higher during both sitting and standing blood pressure measurement positions, the standing blood pressure position caused lower alpha wave frequency when compared with the sitting blood pressure measurement position. While a strong negative correlation was found between alpha wave frequency and pulse rate, beta wave frequency positively correlated with pulse rate during sitting blood pressure measurement position. Furthermore, during standing blood pressure measurement position, the alpha wave did not correlate with pulse rate nor was there a correlation between beta wave frequency and pulse rate. In conclusion, the present study indicated that both sitting and standing blood pressure measurement positions caused a decrease in alpha wave frequency when compared with baseline, but the standing blood pressure measurement position elicited a lower alpha wave frequency when compared with the sitting blood pressure measurement position.

Overcoming the challenge of obtaining reliable heart and pulse rate measurements in reptiles.

Overcoming the challenge of obtaining reliable heart and pulse rate measurements in reptiles.

Impact of Skin Pigmentation on Pulse Oximetry Blood Oxygenation and Wearable Pulse Rate Accuracy: Systematic Review and Meta-Analysis.

Photoplethysmography (PPG) is a technology routinely used in clinical practice to assess blood oxygenation (SpO2) and pulse rate (PR). Skin pigmentation may influence accuracy, leading to health outcomes disparities. This systematic review and meta-analysis primarily aimed to evaluate the accuracy of PPG-derived SpO2 and PR by skin pigmentation. Secondarily, we aimed to evaluate statistical biases and the clinical relevance of PPG-derived SpO2 and PR according to skin pigmentation. We identified 23 pulse oximetry studies (n=59,684; 197,353 paired SpO2-arterial blood observations) and 4 wearable PR studies (n=176; 140,771 paired PPG-electrocardiography observations). We evaluated accuracy according to skin pigmentation group by comparing SpO2 accuracy root-mean-square values to the regulatory threshold of 3% and PR 95% limits of agreement values to +5 or -5 beats per minute (bpm), according to the standards of the American National Standards Institute, Association for the Advancement of Medical Instrumentation, and the International Electrotechnical Commission. We evaluated biases and clinical relevance using mean bias and 95% CI. For SpO2, accuracy root-mean-square values were 3.96%, 4.71%, and 4.15%, and pooled mean biases were 0.70% (95% CI 0.17%-1.22%), 0.27% (95% CI -0.64% to 1.19%), and 1.27% (95% CI 0.58%-1.95%) for light, medium, and dark pigmentation, respectively. For PR, 95% limits of agreement values were from -16.02 to 13.54, from -18.62 to 16.84, and from -33.69 to 32.54, and pooled mean biases were -1.24 (95% CI -5.31 to 2.83) bpm, -0.89 (95% CI -3.70 to 1.93) bpm, and -0.57 (95% CI -9.44 to 8.29) bpm for light, medium, and dark pigmentation, respectively. SpO2 and PR measurements may be inaccurate across all skin pigmentation groups, breaching U.S. Food and Drug Administration guidance and industry standard thresholds. Pulse oximeters significantly overestimate SpO2 for both light and dark skin pigmentation, but this overestimation may not be clinically relevant. PRs obtained from wearables exhibit no statistically or clinically significant bias based on skin pigmentation.

Clinical evaluation of a smart wristband for monitoring oxygen saturation, pulse rate, and respiratory rate.

Recently, photoplethysmography-based vital parameter measurements have increased in popularity. However, clinical evaluation of these measurements is lacking. The objective of this study was to rigorously evaluate the clinical accuracy and reliability of a novel photoplethysmography-based wristband for measuring key vital parameters-oxygen saturation (SpO2), respiratory rate (RR), and pulse rate (PR)-during heart catheterisations. Vital parameters obtained during heart catheterisations by means of a photoplethysmography-based wristband (CardioWatch 287-2, Corsano Health) were compared to reference measurements performed by a Nellcor fingerclip (SpO2, PR) as well as a 5-lead ECG (RR) (QMAPP Haemodynamic Monitoring module, Fysicon B.V.) by means of correlation coefficients and root means squared error (RMSE). Effects of skin colour and arm hair density were additionally evaluated. In total, 945 samples from a total of 100 patients were included in the analysis. The correlation coefficients and RSME obtained for the difference between reference and photoplethysmography-based wristband measurements were r = 0.815 and 1.6% for SpO2, r = 0.976 and 0.9 brpm for RR, and r = 0.995 and 1.3bpm for PR. Similar results were obtained across all skin colour and arm hair density subcategories. This study shows that photoplethysmography-based SpO2, RR, and PR measurements can be accurate during heart catheterisations. Future investigations are required to evaluate the wristband's performance under dynamic circumstances as well as over an extended time period. Trial registration: www.clinicaltrials.gov, NCT05566886.

Improving risk models for patients having emergency bowel cancer surgery using linked electronic health records: a national cohort study

ObjectiveTo investigate whether accuracy of a risk model for colorectal cancer (CRC) patients undergoing emergency surgery, including patient and tumour characteristics from disease-specific data, was improved by inclusion of physiological and surgical measures from linked treatment-specific data. ApproachLinked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed. Results846 of 10,578 (8.0%) patients died within 90 days of surgery. The selected model included seven pre-operative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the ten patient and tumour characteristics in the basic model (year of surgery, age, sex, ASA grade, cancer site, number of comorbidities, emergency admission, TNM T stage, N stage and M stage). The selected model had considerably better discrimination than the basic model (C-statistic: 0.824 versus 0.783, respectively). ConclusionLinkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improved the accuracy of predictions. ImplicationsOur new accurate and relatively simple risk prediction model for patients undergoing emergency CRC surgery will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.

Contactless and Calibration-Free Blood Pressure and Pulse Rate Monitor for Screening and Monitoring of Hypertension: Cross-Sectional Validation Study.

The key to reducing the immense morbidity and mortality burdens of cardiovascular diseases is to help people keep their blood pressure (BP) at safe levels. This requires that more people with hypertension be identified, diagnosed, and given tools to lower their BP. BP monitors are critical to hypertension diagnosis and management. However, there are characteristics of conventional BP monitors (oscillometric cuff sphygmomanometers) that hinder rapid and effective hypertension diagnosis and management. Calibration-free, software-only BP monitors that operate on ubiquitous mobile devices can enable on-demand BP monitoring, overcoming the hardware barriers of conventional BP monitors. This study aims to investigate the accuracy of a contactless BP monitor software app for classifying the full range of clinically relevant BPs as hypertensive or nonhypertensive and to evaluate its accuracy for measuring the pulse rate (PR) and BP of people with BPs relevant to stage-1 hypertension. The software app, known commercially as Lifelight, was investigated following the data collection and data analysis methodology outlined in International Organization for Standardization (ISO) 81060-2:2018/AMD 1:2020 "Non-invasive Sphygmomanometers-Part 2: Clinical investigation of automated measurement type." This validation study was conducted by the independent laboratory Element Materials Technology Boulder (formerly Clinimark). The study generated data from 85 people aged 18-85 years with a wide-ranging distribution of BPs specified in ISO 81060-2:2018/AMD 1:2020. At least 20% were required to have Fitzpatrick scale skin tones of 5 or 6 (ie, dark skin tones). The accuracy of the app's BP measurements was assessed by comparing its BP measurements with measurements made by dual-observer manual auscultation using the same-arm sequential method specified in ISO 81060-2:2018/AMD 1:2020. The accuracy of the app's PR measurements was assessed by comparing its measurements with concurrent electroencephalography-derived heart rate values. The app measured PR with an accuracy root-mean-square of 1.3 beats per minute and mean absolute error of 1.1 (SD 0.8) beats per minute. The sensitivity and specificity with which it determined that BPs exceeded the in-clinic systolic threshold for hypertension diagnosis were 70.1% and 71.7%, respectively. These rates are consistent with those reported for conventional BP monitors in a literature review by The National Institute for Health and Care Excellence. The app's mean error for measuring BP in the range of normotension and stage-1 hypertension (ie, 65/85, 76% of participants) was 6.5 (SD 12.9) mm Hg for systolic BP and 0.4 (SD 10.6) mm Hg for diastolic BP. Mean absolute error was 11.3 (SD 10.0) mm Hg and 8.6 (SD 6.8) mm Hg, respectively. A calibration-free, software-only medical device was independently tested against ISO 81060-2:2018/AMD 1:2020. The safety and performance demonstrated in this study suggest that this technique could be a potential solution for rapid and scalable screening and management of hypertension.

Enhancing Self-Esteem, Well-Being, and Relaxation in the Elderly through Nature-Based Interventions.

As the populations of many countries are aging, institutions providing support for the elderly increasingly often use nature-based interventions (NBIs) as part of their therapeutic activities. This study aimed to show the impact of 8-week active and passive NBI programs on self-esteem, well-being, and relaxation in the elderly. The NBI programs were conducted in two groups of elderly citizens of Poland: independent senior students and seniors requiring 24 h care. The Rosenberg's Self-Esteem Scale and the World Health Organization Well-Being Index were used to assess self-esteem and well-being before and after the programs. To assess relaxation, pre-post session measurements of pulse rate (PR) and blood oxygen saturation (SpO2) were used. Our study showed an improvement in the self-esteem, well-being, and relaxation of the participants of both active and passive NBI programs. In all study groups, self-esteem and well-being improved after the programs, regardless of the type of intervention, and after each NBI session, the elderly showed a decrease in PR and an increase in SpO2, which indicated a deeper state of relaxation. Our research showed a greater improvement in the tested parameters in the seniors participating in the passive programs in a garden. This confirms a key role of green spaces in residential areas for the elderly and in nursing homes.

Effects of Adding Brown Sugar In Young Coconut Water Post High-Intensity Interval Training on Plasma Osmolarity, Oxygen Saturation, and Pulse Rate

This study analyzes the effect of adding brown sugar to young coconut water post-High Intensity Interval Training (HIIT) on plasma osmolarity, oxygen saturation, and pulse rate. Utilizing a pre and post-test control group design, the experiment involved 20 male subjects aged 19-22 with a normal body mass index. The participants were divided into three groups: (K1) receiving mineral water, (K2) receiving young coconut water, and (K3) receiving young coconut water with added brown sugar. Each subject underwent a 30-minute ergo cycle HIIT session. Blood samples were collected at three intervals: before exercise, immediately after, and an hour post-rehydration. The results indicated plasma osmolarity levels showed no significant change from Pre-HIIT (0.939) to Post-HIIT (0.780). However, Post-Rehydration, an increase was observed across the three groups (p=0.004). Pulse rate measurements revealed no significant difference from Pre-HIIT (0.788) to Post-HIIT (0.411), but Post-Rehydration, a significant decrease was noted between the groups (p=0.034). Oxygen saturation levels remained consistent Pre-HIIT (0.902), Post-HIIT (0.602), and 1-hour post-HIIT (0.611) within the third group. In conclusion, adding brown sugar to young coconut water post-HIIT resulted in higher plasma osmolarity, did not affect oxygen saturation levels, and contributed to a lower pulse rate. Keyword: Plasma osmolarity; oxygen saturation; pulse rate; brown sugar; young coconut water

IoT Based Medical Assessor and Predictor for HealthCare Solutions

Abstract: The introduction of cutting-edge, intelligent medical equipment into the virtual doctor system is orchestrating a remarkable transformation within community healthcare. Research in the healthcare sector has unveiled promising developments in extending human lifespan. However, obtaining healthcare services in remote areas remains a formidable challenge as patients often have to endure long journeys for treatment. The incorporation of IoT technology offers a viable solution by allowing real-time monitoring of vital signs through sensors like heart rate, Spo2, temperature, B.P and ECG sensors, measuring pulse rate, blood oxygen levels, body temperature, and blood pressure, respectively and applying different machine learning algorithms to predict the condition of the patient and suggest some precautions & medications. The reliability of the collected data is assured, and ThingSpeak software transmits this data to the cloud, enabling the delivery of professional healthcare services to those in need. Notably, statistics reveal that in India, the doctor-to-patient ratio stands at 1:854, underscoring the crucial role of this technology in addressing healthcare requirements in rural communities.

Multimodal electrohydrodynamic jet printing-based microstructure-sensitized flexible pressure sensor

Surface modification with micro/nanostructures is a common approach for enhancing the performance of flexible pressure sensors. However, the current fabrication of the singular functionality of instruments and redundancy of processes increase the complexity of the sensor manufacturing process. In this study, we developed a multilayer microstructure-enhanced flexible capacitive pressure sensor based on the multimodal electrohydrodynamic jet (E-jet) printing technology. The experimental results demonstrate that the sensors incorporating the microstructure-sensitized electrode layer and the polyvinyl alcohol/graphene/polydimethylsiloxane dielectric layer exhibit the following characteristics: high sensitivity (0.3139 kPa−1/0–2 kPa), low limit of detection (∼100 mg), and stable performance even after 10,000 cycles. Moreover, microstructure-enhanced sensors have considerable potential for human behavior detection, such as detecting fluid flow, tracking muscle movements, and measuring pulse rates. Finally, microstructure-enhanced sensors fabricated using the E-jet printing method present a novel approach for designing sensitized structures in capacitive pressure sensors.

Hospital Environmental Factors That Influence Peripheral Oxygen Saturation Measurements: A Cross-Sectional Study.

Pulse oximetry is a non-invasive, cost-effective, and generally reliable instrument measuring pulse rate and peripheral oxygen saturation (SpO2). However, these measurements can be affected by the patient's internal or external factors, including the type of pulse oximeter device (POD). (1) This study's objective was to identify potential environmental factors that may impact the measurements taken by three PODs. (2) Methods: A descriptive-analytical cross-sectional study was designed. The patients' SpO2 levels were measured using a standard monitor and two PODs owned by the professionals. The measurements were taken on the patients' fingers. Concurrently, we evaluated the surrounding environmental conditions, encompassing temperature, humidity, illuminance, and noise. (3) Results: This study involved 288 adult participants in the sample. For each 20-decibel increment in noise, there was a reduction in SpO2 by an average of 1%, whereas for every additional degree of ambient temperature, SpO2 decreased by an average of 2% (4) Conclusions: Significant correlations between SpO2 and age, as well as with noise and ambient temperature, were observed. No significant differences between oxygen saturation and lighting or humidity were observed. This study was prospectively registered with the Clinical Research Ethics Committee of Gran Canaria at the Dr. Negrín University Hospital, with protocol code 2019-247-1, and approved on 24 May 2019.

From video to vital signs: using personal device cameras to measure pulse rate and predict blood pressure using explainable AI

In this study, we address the need for low-cost, rapid, and reliable methods for blood pressure measurement to facilitate timely diagnosis and treatment of hypertension. We aim to enhance understanding and trust in the novel remote photoplethysmography technology, specifically as deployed by Lifelight®\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circledR$$\\end{document}, for vital sign monitoring. Lifelight®\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circledR$$\\end{document} utilizes remote photoplethysmography to extract physiologically meaningful features from video recordings on personal devices. This study describes the accurate calculation of pulse rates and the extraction of morphological and time series features from the remote photoplethysmography signal for blood pressure prediction. Unlike common approaches, distinct model types are employed for predicting systolic and diastolic blood pressure. Evaluation on an independent clinical laboratory’s dataset demonstrates the efficacy of the proposed method. The pulse rate accuracy, measured as root mean square error, is 1.9 beats per minute. The model for systolic blood pressure prediction shows a mean error of 1.2 mmHg and a standard deviation of 15.9 mmHg. The model for diastolic prediction shows a mean error of -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}3.2 mmHg and a standard deviation of 13.1 mmHg. This research establishes the validity of remote photoplethysmography technology by showcasing accurate pulse rate calculations and effective feature extraction for blood pressure prediction. The results indicate the potential for widespread adoption of remote photoplethysmography in measuring blood pressure and other vital signs.

Improving risk models for patients having emergency bowel cancer surgery using linked electronic health records: a national cohort study.

Life-saving emergency major resection of colorectal cancer (CRC) is a high-risk procedure. Accurate prediction of postoperative mortality for patients undergoing this procedure is essential for both healthcare performance monitoring and preoperative risk assessment. Risk-adjustment models for CRC patients often include patient and tumour characteristics, widely available in cancer registries and audits. The authors investigated to what extent inclusion of additional physiological and surgical measures, available through linkage or additional data collection, improves accuracy of risk models. Linked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed. Eight hundred forty-six of 10578 (8.0%) patients died within 90 days of surgery. The selected model included seven preoperative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the 10 patient and tumour characteristics in the basic model (calendar year of surgery, age, sex, ASA grade, TNM T stage, TNM N stage, TNM M stage, cancer site, number of comorbidities, and emergency admission). The selected model had considerably better discrimination compared to the basic model (C-statistic: 0.824 versus 0.783, respectively). Linkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improves the accuracy of the prediction of the mortality risk for CRC patients having emergency surgery. This improvement will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.

Performance enhancement of thermal image analysis for noncontact cardiopulmonary signal extraction

Infrared thermography (IRT) has emerged as a tool for the rapid and non-contact detection of febrile individuals in places of mass gathering. This study explores the feasibility of extracting cardiopulmonary signals (i.e., respiration and cardiac pulse signals) by analyzing thermal image sequences. A high IR spatial resolution (320 × 240 pixels) IRT was applied to acquire facial thermal image sequences at a frame rate of 60 frames per second (fps). The respiration signal was extracted by monitoring the temperature changes in the region of interest (ROI) of the nostrils caused by breathing, that is, the temperature increase during the exhalation phase and the temperature decrease during the inhalation phase. The respiration signal was extracted by picking up the rise and fall motions of the shoulders caused by breathing. The cardiac pulse signal was extracted by measuring tiny head motions accompanying the cardiac cycle on the thermal image sequences. The system was tested on 11 subjects in a laboratory setting to assess the respiration performance and pulse signal extraction. Bland–Altman and Pearson's correlation plots were used to compare the noncontact respiration rate (RR) and pulse rate (PR) measurements with contact-type ECG and respiration belt. The mean difference in the RR was − 0.2 bpm with the 95 % limit of − 5.0 to 4.6 bpm, correlation of 0.83, root mean square error (RMSE) of 2.4 bpm, and mean absolute percentage error (MAPE) of 22 %. Regarding the PR, the mean difference of 4.2 bpm with the 95 % limit of − 18 to 26 bpm, correlation of 0.67, RMSE of 12 bpm, and MAPE of 15 % were determined from comparison of the ECG and thermal camera. In summary, our findings suggest that the proposed thermal image analysis method for measuring RR and PR is promising, particularly for RR measurements. Further validation and refinement are required for the PR measurements. The results indicate that this noncontact approach has potential for various medical applications.