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Paul S Addison - One of the best experts on this subject based on the ideXlab platform.
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multicenter study validating accuracy of a continuous Respiratory Rate measurement derived from pulse oximetry a comparison with capnography
Anesthesia & Analgesia, 2017Co-Authors: Sergio D. Bergese, Michael L. Mestek, Alberto A. Uribe, James Nicholas Watson, Scott Kelley, Robert C Mcintyre, Rakesh Sethi, Paul S AddisonAbstract:BACKGROUND: Intermittent measurement of Respiratory Rate via observation is routine in many patient care settings. This approach has several inherent limitations that diminish the clinical utility of these measurements because it is intermittent, susceptible to human error, and requires clinical resources. As an alternative, a software application that derives continuous Respiratory Rate measurement from a standard pulse oximeter has been developed. We sought to determine the performance characteristics of this new technology by comparison with clinician-reviewed capnography waveforms in both healthy subjects and hospitalized patients in a low-acuity care setting.
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developing an algorithm for pulse oximetry derived Respiratory Rate rr oxi a healthy volunteer study
Journal of Clinical Monitoring and Computing, 2012Co-Authors: Paul S Addison, Michael L. Mestek, James Nicholas Watson, Roger MeccaAbstract:Objective The presence of Respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring Respiratory Rate requires: (1) the recognition of the multi-faceted manifestations of Respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported Respiratory Rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RROXI algorithm based on this principle and its performance on healthy subject trial data is described herein.
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developing an algorithm for pulse oximetry derived Respiratory Rate rr oxi a healthy volunteer study
Journal of Clinical Monitoring and Computing, 2012Co-Authors: Paul S Addison, Michael L. Mestek, James Nicholas Watson, Roger S MeccaAbstract:Objective The presence of Respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring Respiratory Rate requires: (1) the recognition of the multi-faceted manifestations of Respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported Respiratory Rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RROXI algorithm based on this principle and its performance on healthy subject trial data is described herein. Methods Finger PPGs were collected from a cohort of 139 healthy adult volunteers monitored during free breathing over an 8-min period. These were subsequently processed using a novel in-house algorithm based on continuous wavelet transform technology within an infrastructure incorporating weighted averaging and logical decision making processes. The computed oximeter Respiratory Rates (RRoxi) were then compared to an end-tidal CO2 reference Rate ( $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$ ). Results $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$ ranged from a lowest recorded value of 2.97 breaths per min (br/min) to a highest value of 28.02 br/min. The mean Rate was 14.49 br/min with standard deviation of 4.36 br/min. Excellent agreement was found between RRoxi and $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$ , with a mean difference of −0.23 br/min and standard deviation of 1.14 br/min. The two measures are tightly spread around the line of agreement with a strong correlation observable between them (R 2 = 0.93). Conclusions These data indicate that RRoxi represents a viable technology for the measurement of Respiratory Rate of healthy individuals.
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A Fully Automated Algorithm for the Determination of Respiratory Rate from the Photoplethysmogram
Journal of Clinical Monitoring and Computing, 2006Co-Authors: Paul A. Leonard, Paul S Addison, J. Graham Douglas, Neil R. Grubb, David Clifton, James N. WatsonAbstract:Objective. To determine if an automatic algorithm using wavelet analysis techniques can be used to reliably determine Respiratory Rate from the photoplethysmogram (PPG). Methods. Photoplethysmograms were obtained from 12 spontaneously breathing healthy adult volunteers. Three related wavelet transforms were automatically polled to obtain a measure of Respiratory Rate. This was compared with a secondary timing signal obtained by asking the volunteers to actuate a small push button switch, held in their right hand, in synchronisation with their respiration. In addition, individual breaths were resolved using the wavelet-method to identify the source of any discrepancies. Results. Volunteer Respiratory Rates varied from 6.56 to 18.89 breaths per minute. Through training of the algorithm it was possible to determine a Respiratory Rate for all 12 traces acquired during the study. The maximum error between the PPG derived Rates and the manually determined Rate was found to be 7.9%. Conclusion. Our technique allows the accuRate measurement of Respiratory Rate from the photoplethysmogram, and leads the way for developing a simple non-invasive combined respiration and saturation monitor.
Heather Elphick - One of the best experts on this subject based on the ideXlab platform.
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Exploratory study to evaluate Respiratory Rate using a thermal imaging camera
Respiration, 2019Co-Authors: Heather Elphick, Abdulkadir Hamidu Alkali, Ruth K. Kingshott, Derek Burke, Reza SaatchiAbstract:Background: Respiratory Rate is a vital physiological measurement used in the immediate assessment of unwell children and adults. Convenient electronic devices exist for measurement of pulse, blood pressure, oxygen saturation and temperature. Although devices which measure Respiratory Rate exist, none has entered everyday clinical practice for acute assessment of children and adults. An accuRate and practical device which has no physical contact with the patient is important to ensure readings are not affected by distress caused by the assessment method. Objective: To evaluate the use of a thermal imaging method to monitor Respiratory Rate in children and adults. Methods: Facial thermal images of adult volunteers and children undergoing elective polysomnography were included. Respiration was recorded for at least two minutes with the camera positioned one metre from the subject's face. Values obtained using the thermal imaging camera were compared with those obtained from contact methods such as nasal thermistor, Respiratory inductance plethysmography, nasal airflow and End Tidal Carbon Dioxide (CO2). Results: A total of 61 subjects, including 41 adults (age range 27 to 46 years) and 20 children (age range 0.5 to 18 years) were enrolled. The correlation between Respiratory Rate measured using thermal imaging and the contact method was r=0.94. Sequential refinements to the thermal imaging algorithms resulted in the ability to perform real-time measurements and an improvement of the correlation to r=0.995. Conclusion: This exploratory study shows that thermal imaging derived Respiratory Rates in children and adults correlate closely with the best performing standard method. With further refinements, this method could be implemented in both acute and chronic care in children and adults.
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contactless portable Respiratory Rate monitor cprm accuRately measures Respiratory Rate in children
European Respiratory Journal, 2016Co-Authors: Will Daw, Ruth K. Kingshott, Reza Sachi, Heather ElphickAbstract:Background: Respiratory Rate (RR) is an important vital sign used in the initial and ongoing assessment of acutely ill children. It is also used as a predictor of serious deterioration in a patient9s clinical condition. Convenient electronic devices exist for measurement of pulse, blood pressure, oxygen saturation and temperature. Although devices which measure RR exist, none has entered everyday clinical practice. Aims: We have developed a contactless portable Respiratory Rate monitor (CPRM) and aimed to evaluate the agreement in RR measurements between existing methods and our new device. Method: RR data were collected from 30 children undergoing polysomnography sleep studies at a tertiary children9s hospital. The RR of each participant was measured simultaneously by 3 different methods. Respiratory impedance plethysmography (RIP) the established contact method and gold standard. Visual counting of chest movements, and method used most in clinical settings. The contactless portable Respiratory monitor (CPRM), which is the new method. Two data sets were collected from each child. Results: Data showed substantial agreement between measurements from the CPRM and the gold standard RIP, with intra-class correlation coefficient: 0.762, mean difference -0.212 and 95% limits of agreement of -6.842 to 6.419. Conclusion: A contactless device for accuRately and quickly measuring RR will be an important tool in the assessment of unwell children. More testing is needed to explore the reasons for outlying measurements and to evaluate in different clinical settings. Further development and modification of the device and software are ongoing.
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development of the breatheasy contactless portable Respiratory Rate monitor cprm
European Respiratory Journal, 2015Co-Authors: Will Daw, Reza Saatchi, Ruth K. Kingshott, Alison Scott, Heather ElphickAbstract:Background: Respiratory Rate (RR) is a vital physiological measurement used in the immediate assessment of acutely ill patients. It is used as a predictor of serious deterioration in a patient's clinical condition. Convenient electronic devices exist for measurement of pulse, blood pressure, oxygen saturation and temperature. Although devices which measure RR exist, none has entered everyday clinical practice. We have developed a contactless portable Respiratory Rate monitor (CPRM). We aimed to measure agreement with existing methods of RR measurement. Method: RR data were collected from 33 adult volunteers using Respiratory impedance plethysmography (RIP) bands (established contact method), visual counting of chest movements (established non-contact method) and the CPRM (new method), simultaneously. Two to three data sets were collected for each volunteer. Results: Data showed good agreement between measurements from the CPRM and the gold standard RIP with limits of agreement -4.6 – 7.8. Conclusion: A contactless device for accuRately and quickly measuring RR will be an important tool in the assessment of unwell children. More testing is needed to explore reasons for outlying measurements and to evaluate in the paediatric population. Further development and modification of the device and software are planned.
Arthas Flabouris - One of the best experts on this subject based on the ideXlab platform.
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Respiratory Rate: The neglected vital sign
Medical Journal of Australia, 2008Co-Authors: Michelle A. Cretikos, Rinaldo Bellomo, Jack Chen, Simon Finfer, Ken Hillman, Arthas FlabourisAbstract:The level of documentation of vital signs in many hospitals is extremely poor, and Respiratory Rate, in particular, is often not recorded. There is substantial evidence that an abnormal Respiratory Rate is a predictor of potentially serious clinical events. Nurses and doctors need to be more aware of the importance of an abnormal Respiratory Rate as a marker of serious illness. Hospital systems that encourage appropriate responses to an elevated Respiratory Rate and other abnormal vital signs can be rapidly implemented. Such systems help to raise and sustain awareness of the importance of vital signs.
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Respiratory Rate the neglected vital sign commentary
The Medical Journal of Australia, 2008Co-Authors: James D Cooper, Michelle A. Cretikos, Rinaldo Bellomo, Jack Chen, Simon Finfer, Ken Hillman, Michael Buist, Arthas FlabourisAbstract:• The level of documentation of vital signs in many hospitals is extremely poor, and Respiratory Rate, in particular, is often not recorded. • There is substantial evidence that an abnormal Respiratory Rate is a predictor of potentially serious clinical events. • Nurses and doctors need to be more aware of the importance of an abnormal Respiratory Rate as a marker of serious illness. • Hospital systems that encourage appropriate responses to an elevated Respiratory Rate and other abnormal vital signs can be rapidly implemented. Such systems help to raise and sustain awareness of the importance of vital signs.
Michael L. Mestek - One of the best experts on this subject based on the ideXlab platform.
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multicenter study validating accuracy of a continuous Respiratory Rate measurement derived from pulse oximetry a comparison with capnography
Anesthesia & Analgesia, 2017Co-Authors: Sergio D. Bergese, Michael L. Mestek, Alberto A. Uribe, James Nicholas Watson, Scott Kelley, Robert C Mcintyre, Rakesh Sethi, Paul S AddisonAbstract:BACKGROUND: Intermittent measurement of Respiratory Rate via observation is routine in many patient care settings. This approach has several inherent limitations that diminish the clinical utility of these measurements because it is intermittent, susceptible to human error, and requires clinical resources. As an alternative, a software application that derives continuous Respiratory Rate measurement from a standard pulse oximeter has been developed. We sought to determine the performance characteristics of this new technology by comparison with clinician-reviewed capnography waveforms in both healthy subjects and hospitalized patients in a low-acuity care setting.
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Pulse oximetry-derived Respiratory Rate in general care floor patients
Journal of Clinical Monitoring and Computing, 2014Co-Authors: Paul Stanley Addison, James N. Watson, Michael L. Mestek, James P. Ochs, Alberto A. Uribe, Sergio D. BergeseAbstract:Respiratory Rate is recognized as a clinically important parameter for monitoring Respiratory status on the general care floor (GCF). Currently, intermittent manual assessment of Respiratory Rate is the standard of care on the GCF. This technique has several clinically-relevant shortcomings, including the following: (1) it is not a continuous measurement, (2) it is prone to observer error, and (3) it is inefficient for the clinical staff. We report here on an algorithm designed to meet clinical needs by providing Respiratory Rate through a standard pulse oximeter. Finger photoplethysmograms were collected from a cohort of 63 GCF patients monitored during free breathing over a 25-min period. These were processed using a novel in-house algorithm based on continuous wavelet-transform technology within an infrastructure incorporating confidence-based averaging and logical decision-making processes. The computed oximeter Respiratory Rates (RRoxi) were compared to an end-tidal CO2 reference Rate (RRETCO2). RRETCO2 ranged from a lowest recorded value of 4.7 breaths per minute (brpm) to a highest value of 32.0 brpm. The mean Respiratory Rate was 16.3 brpm with standard deviation of 4.7 brpm. Excellent agreement was found between RRoxi and RRETCO2, with a mean difference of -0.48 brpm and standard deviation of 1.77 brpm. These data demonstRate that our novel Respiratory Rate algorithm is a potentially viable method of monitoring Respiratory Rate in GCF patients. This technology provides the means to facilitate continuous monitoring of Respiratory Rate, coupled with arterial oxygen saturation and pulse Rate, using a single non-invasive sensor in low acuity settings.
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developing an algorithm for pulse oximetry derived Respiratory Rate rr oxi a healthy volunteer study
Journal of Clinical Monitoring and Computing, 2012Co-Authors: Paul S Addison, Michael L. Mestek, James Nicholas Watson, Roger MeccaAbstract:Objective The presence of Respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring Respiratory Rate requires: (1) the recognition of the multi-faceted manifestations of Respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported Respiratory Rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RROXI algorithm based on this principle and its performance on healthy subject trial data is described herein.
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developing an algorithm for pulse oximetry derived Respiratory Rate rr oxi a healthy volunteer study
Journal of Clinical Monitoring and Computing, 2012Co-Authors: Paul S Addison, Michael L. Mestek, James Nicholas Watson, Roger S MeccaAbstract:Objective The presence of Respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring Respiratory Rate requires: (1) the recognition of the multi-faceted manifestations of Respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported Respiratory Rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RROXI algorithm based on this principle and its performance on healthy subject trial data is described herein. Methods Finger PPGs were collected from a cohort of 139 healthy adult volunteers monitored during free breathing over an 8-min period. These were subsequently processed using a novel in-house algorithm based on continuous wavelet transform technology within an infrastructure incorporating weighted averaging and logical decision making processes. The computed oximeter Respiratory Rates (RRoxi) were then compared to an end-tidal CO2 reference Rate ( $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$ ). Results $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$ ranged from a lowest recorded value of 2.97 breaths per min (br/min) to a highest value of 28.02 br/min. The mean Rate was 14.49 br/min with standard deviation of 4.36 br/min. Excellent agreement was found between RRoxi and $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$ , with a mean difference of −0.23 br/min and standard deviation of 1.14 br/min. The two measures are tightly spread around the line of agreement with a strong correlation observable between them (R 2 = 0.93). Conclusions These data indicate that RRoxi represents a viable technology for the measurement of Respiratory Rate of healthy individuals.
Emily R Capodilupo - One of the best experts on this subject based on the ideXlab platform.
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analyzing changes in Respiratory Rate to predict the risk of covid 19 infection
PLOS ONE, 2020Co-Authors: Dean J Miller, John V Capodilupo, Michele Lastella, Charli Sargent, Gregory D Roach, Victoria Lee, Emily R CapodilupoAbstract:COVID-19, the disease caused by the SARS-CoV-2 virus, can cause shortness of breath, lung damage, and impaired Respiratory function. Containing the virus has proven difficult, in large part due to its high transmissibility during the pre-symptomatic incubation. The study's aim was to determine if changes in Respiratory Rate could serve as a leading indicator of SARS-CoV-2 infections. A total of 271 individuals (age = 37.3 ± 9.5, 190 male, 81 female) who experienced symptoms consistent with COVID-19 were included- 81 tested positive for SARS-CoV-2 and 190 tested negative; these 271 individuals collectively contributed 2672 samples (days) of data (1856 healthy days, 231 while infected with COVID-19 and 585 while negative for COVID-19 but experiencing symptoms). To train a novel algorithm, individuals were segmented as follows; (1) a training dataset of individuals who tested positive for COVID-19 (n = 57 people, 537 samples); (2) a validation dataset of individuals who tested positive for COVID-19 (n = 24 people, 320 samples); (3) a validation dataset of individuals who tested negative for COVID-19 (n = 190 people, 1815 samples). All data was extracted from the WHOOP system, which uses data from a wrist-worn strap to produce validated estimates of Respiratory Rate and other physiological measures. Using the training dataset, a model was developed to estimate the probability of SARS-CoV-2 infection based on changes in Respiratory Rate during night-time sleep. The model's ability to identify COVID-positive individuals not used in training and robustness against COVID-negative individuals with similar symptoms were examined for a critical six-day period spanning the onset of symptoms. The model identified 20% of COVID-19 positive individuals in the validation dataset in the two days prior to symptom onset, and 80% of COVID-19 positive cases by the third day of symptoms.
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analyzing changes in Respiratory Rate to predict the risk of covid 19 infection
medRxiv, 2020Co-Authors: Dean J Miller, John V Capodilupo, Michele Lastella, Charli Sargent, Gregory D Roach, Victoria Lee, Emily R CapodilupoAbstract:COVID-19, the disease caused by the SARS-CoV-2 virus, can cause shortness of breath, lung damage, and impaired Respiratory function. Containing the virus has proven difficult, in large part due to its high transmissibility during the pre-symptomatic incubation. The study9s aim was to determine if changes in Respiratory Rate could serve as a leading indicator of SARS-CoV-2 infections. A total of 271 individuals (age = 37.3 ± 9.5, 190 male, 81 female) who experienced symptoms consistent with COVID-19 were included - 81 tested positive for SARS-CoV-2 and 190 tested negative; these 271 individuals collectively contributed 2672 samples (days) of data (1856 healthy days, 231 while infected with COVID-19 and 585 while infected with something other than COVID-19). To train a novel algorithm, individuals were segmented as follows; (1) a training dataset of individuals who tested positive for COVID-19 (n=57 people, 537 samples); (2) a validation dataset of individuals who tested positive for COVID-19 (n=24 people, 320 samples) ; (3) a validation dataset of individuals who tested negative for COVID-19 (n=190 people, 1815 samples). All data was extracted from the WHOOP system, which uses data from a wrist-worn strap to produce validated estimates of Respiratory Rate and other physiological measures. Using the training dataset, a model was developed to estimate the probability of SARS-CoV-2 infection based on changes in Respiratory Rate during night-time sleep. The model9s ability to identify COVID-positive individuals not used in training and robustness against COVID-negative individuals with similar symptoms were examined for a critical six-day period spanning the onset of symptoms. The model identified 20% of COVID-19 positive individuals in the validation dataset in the two days prior to symptom onset, and 80% of COVID-19 positive cases by the third day of symptoms.
