scholarly journals Non-contact vital-sign monitoring of patients undergoing haemodialysis treatment

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mauricio Villarroel ◽  
João Jorge ◽  
David Meredith ◽  
Sheera Sutherland ◽  
Chris Pugh ◽  
...  
Keyword(s):  
Heart Rate ◽  
Respiratory Rate ◽  
Reference Values ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Recording Time ◽  
Wide Range ◽  
Auto Regressive ◽  
The Mean ◽  
Haemodialysis Treatment

Abstract A clinical study was designed to record a wide range of physiological values from patients undergoing haemodialysis treatment in the Renal Unit of the Churchill Hospital in Oxford. Video was recorded for a total of 84 dialysis sessions from 40 patients during the course of 1 year, comprising an overall video recording time of approximately 304.1 h. Reference values were provided by two devices in regular clinical use. The mean absolute error between the heart rate estimates from the camera and the average from two reference pulse oximeters (positioned at the finger and earlobe) was 2.8 beats/min for over 65% of the time the patient was stable. The mean absolute error between the respiratory rate estimates from the camera and the reference values (computed from the Electrocardiogram and a thoracic expansion sensor—chest belt) was 2.1 breaths/min for over 69% of the time for which the reference signals were valid. To increase the robustness of the algorithms, novel methods were devised for cancelling out aliased frequency components caused by the artificial light sources in the hospital, using auto-regressive modelling and pole cancellation. Maps of the spatial distribution of heart rate and respiratory rate information were developed from the coefficients of the auto-regressive models. Most of the periods for which the camera could not produce a reliable heart rate estimate lasted under 3 min, thus opening the possibility to monitor heart rate continuously in a clinical environment.

scholarly journals Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mauricio Villarroel ◽  
Sitthichok Chaichulee ◽  
João Jorge ◽  
Sara Davis ◽  
Gabrielle Green ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Respiratory Rate ◽  
Preterm Infants ◽  
Neonatal Intensive Care ◽  
Mean Absolute Error ◽  
Vital Signs ◽  
Absolute Error ◽  
Video Camera ◽  
The Mean

AbstractThe implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.

scholarly journals Prospective Validation of Smartphone-based Heart Rate and Respiratory Rate Measurement Algorithms

2021 ◽  
Author(s):  
Sean Bae ◽  
Silviu Borac ◽  
Yunus Emre ◽  
Jonathan Wang ◽  
Jiang Wu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Respiratory Rate ◽  
Vital Signs ◽  
Absolute Error ◽  
Medical Settings ◽  
Post Exercise ◽  
Specialized Equipment ◽  
The Mean ◽  
Respiratory Conditions ◽  
Absolute Percent Error

Abstract Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breath/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.

scholarly journals Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms

2021 ◽  
Author(s):  
Sean Bae ◽  
Silviu Borac ◽  
Yunus Emre ◽  
Jonathan Wang ◽  
Jiang Wu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Respiratory Rate ◽  
Vital Signs ◽  
Absolute Error ◽  
Medical Settings ◽  
Post Exercise ◽  
Specialized Equipment ◽  
The Mean ◽  
Respiratory Conditions ◽  
Absolute Percent Error

AbstractMeasuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breath/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). Our results validate that smartphone camera-based techniques can accurately measure HR and RR across a range of pre-defined subgroups.

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

scholarly journals FRI0618-HPR EFFECTS OF MUSIC THERAPY ON PAIN, ANXIETY, AND VITAL SIGNS IN CHRONIC INFLAMMATORY RHEUMATIC DISEASES PATIENTS DURING BIOLOGICAL DRUGS INFUSION

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 914.2-914
Author(s):  
S. Boussaid ◽  
M. Ben Majdouba ◽  
S. Jriri ◽  
M. Abbes ◽  
S. Jammali ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Music Therapy ◽  
Systolic Blood Pressure ◽  
Respiratory Rate ◽  
Vital Signs ◽  
Biological Therapies ◽  
Biological Drugs ◽  
Pain Anxiety ◽  
The Mean

Background:Music therapy is based on ancient cross-cultural beliefs that music can have a “healing” effect on mind and body. Research determined that listening to music can increase comfort and relaxation, relieve pain, lower distress, reduce anxiety, improve positive emotions and mood, and decrease psychological symptoms. Music therapy has been used greatly in various medical procedures to reduce associated anxiety and pain. Patients have a high level of anxiety when they are in the hospital, this is the case of patients with rheumatic diseases who consult regularly to have intravenous infusion of biological therapies.Objectives:The purpose of this study was to examine the effectiveness of music therapy on pain, anxiety, and vital signs among patients with chronic inflammatory rheumatic diseases during intravenous infusion of biological drugs.Methods:Fifty patients were divided into two groups: The experimental group G1 (n=25) received drug infusion while lestening to soft music (30 minutes); and the control group G2 (n=25) received only drug infusion. Measures include pain, anxiety, vital signs (blood pressure, heart rate and respiratory rate). The pain was measured using visual analogic scale (VAS). The state-trait anxiety inventory (STAI) was used for measuring anxiety, low anxiety ranges from 20 to 39, the moderate anxiety ranges from 40 to 59, and high anxiety ranges from 60 to 80. Vital signs (systolic blood pressure [SBP], diastolic blood pressure [DBP], heart rate [HR], and respiratory rate [RR]) were measured before, during and immediately after the infusion.Statistical package for social sciences (SPSS) was used for analysis.Results:The mean age in G1 was 44.45 years (26-72) with a sex ratio (M/F) of 0.8. Including the 25 patients, 12 had rheumatoid arthritis, 10 had ankylosing spondylitis and 3 had psoriatic arthritis. The mean disease duration was 8 years. In G2, the mean age was 46 years (25-70) with a sex ratio (M/F) of 0.75, 12 had rheumatoid arthritis, 11 had ankylosing spondylitis and 2 had psoriatic arthritis. The mean disease duration was 7.5 years. The biological drugs used were: Infliximab in 30 cases, Tocilizumab in 12 cases and Rituximab in 8 cases.Before the infusion, the patients of experimental group had a mean VAS of 5/10±3, a mean STAI of 50.62±6.01, a mean SBP of 13.6 cmHg±1.4, a mean DBP of 8.6 cmHg±1, a mean HR of 85±10 and a mean RR of 18±3. While in control group the mean VAS was 5.5±2, the mean STAI was 50.89±5.5, the mean SBP was 13.4±1.2, the mean DBP was 8.8±1.1, the mean HR was 82±8 and the mean RR was 19±2.During the infusion and after music intervention in G1, the mean STAI became 38.35±5 in G1 versus 46.7±5.2 in G2 (p value=0.022), the mean SBP became 12.1±0.5 in G1 versus 13±1 in G2 (p=0.035), the mean DBP became 8.1±0.8 in G1 versus 8.4±0.9 in G2 (p=0.4), the mean HR became 76±9 in G1 versus 78±7 in G2 (p=0.04) and the mean RR became 17.3±2.1 in G1 versus 18.2±1.7 in G2 (p=0.39).This study found a statistically significant decrease in anxiety, systolic blood pressure and heart rate in patients receiving music interventions during biological therapies infusion, but no significant difference were identified in diastolic blood pressure and respiratory rate.Conclusion:The findings provide further evidence to support the use of music therapy to reduce anxiety, and lower systolic blood pressure and heart rate in patients with rheumatic disease during biological therapies infusion.References:[1] Lin, C., Hwang, S., Jiang, P., & Hsiung, N. (2019).Effect of Music Therapy on Pain After Orthopedic Surgery -A Systematic review and Meta-Analysis. Pain Practice.Disclosure of Interests:None declared

Visualizing the Variance of a Random Variable

2011 ◽  
Vol 18 (01) ◽  
pp. 71-85
Author(s):  
Fabrizio Cacciafesta
Keyword(s):  
Stochastic Dominance ◽  
Mean Absolute Error ◽  
Random Variable ◽  
Absolute Error ◽  
Second Order ◽  
Taylor Formula ◽  
Order Stochastic Dominance ◽  
The Mean ◽  
Second Order Stochastic Dominance ◽  
Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

scholarly journals Direct Calculation of Thermodynamic Wet-Bulb Temperature as a Function of Pressure and Elevation

2013 ◽  
Vol 30 (8) ◽  
pp. 1757-1765 ◽  
Author(s):  
Sayed-Hossein Sadeghi ◽  
Troy R. Peters ◽  
Douglas R. Cobos ◽  
Henry W. Loescher ◽  
Colin S. Campbell
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Direct Calculation ◽  
Ambient Air ◽  
Absolute Error ◽  
Mean Square ◽  
Air Temperatures ◽  
Order Polynomial ◽  
The Mean ◽  
Very High

Abstract A simple analytical method was developed for directly calculating the thermodynamic wet-bulb temperature from air temperature and the vapor pressure (or relative humidity) at elevations up to 4500 m above MSL was developed. This methodology was based on the fact that the wet-bulb temperature can be closely approximated by a second-order polynomial in both the positive and negative ranges in ambient air temperature. The method in this study builds upon this understanding and provides results for the negative range of air temperatures (−17° to 0°C), so that the maximum observed error in this area is equal to or smaller than −0.17°C. For temperatures ≥0°C, wet-bulb temperature accuracy was ±0.65°C, and larger errors corresponded to very high temperatures (Ta ≥ 39°C) and/or very high or low relative humidities (5% < RH < 10% or RH > 98%). The mean absolute error and the root-mean-square error were 0.15° and 0.2°C, respectively.

scholarly journals Comparative verification in complex topography

2021 ◽  
Author(s):  
Jonas Bhend ◽  
Jean-Christophe Orain ◽  
Vera Schönenberger ◽  
Christoph Spirig ◽  
Lionel Moret ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Absolute Error ◽  
Time Of Day ◽  
Probabilistic Forecasts ◽  
Wide Range ◽  
Direct Model ◽  
Forecasting System ◽  
The Mean ◽  
Update Frequency ◽  
Core Activity

<p>Verification is a core activity in weather forecasting. Insights from verification are used for monitoring, for reporting, to support and motivate development of the forecasting system, and to allow users to maximize forecast value. Due to the broad range of applications for which verification provides valuable input, the range of questions one would like to answer can be very large. Static analyses and summary verification results are often insufficient to cover this broad range. To this end, we developed an interactive verification platform at MeteoSwiss that allows users to inspect verification results from a wide range of angles to find answers to their specific questions.</p><p>We present the technical setup to achieve a flexible yet performant interactive platform and two prototype applications: monitoring of direct model output from operational NWP systems and understanding of the capabilities and limitations of our pre-operational postprocessing. We present two innovations that illustrate the user-oriented approach to comparative verification adopted as part of the platform. To facilitate the comparison of a broad range of forecasts issued with varying update frequency, we rely on the concept of time of verification to collocate the most recent available forecasts at the time of day at which the forecasts are used. In addition, we offer a matrix selection to more flexibly select forecast sources and scores for comparison. Doing so, we can for example compare the mean absolute error (MAE) for deterministic forecasts to the MAE and continuous ranked probability scores of probabilistic forecasts to illustrate the benefit of using probabilistic forecasts.</p>

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