This poster provides an overview of the work described in:

P. H. Charlton, "Continuous respiratory rate monitoring to detect clinical deteriorations using wearable sensors," Ph.D. Thesis, King’s College London, 2017.This poster was first presented at the Bioengenuity Keynotes Conference, held on Monday 6th March at the University of Oxford

Alastruey-Arimon, Jordi

Beale, Richard

Bonnici, Timothy Alexander

Charlton, Peter Harcourt

Clifton, David A.

Tarassenko, Lionel

Watkinson, Peter J.

English

King's Research Portal

                       King’s Research Portal  DOI:10.5281/zenodo.1064813Document VersionPublisher's PDF, also known as Version of recordLink to publication record in King's Research PortalCitation for published version (APA):Charlton, P. H., Bonnici, T. A., Tarassenko, L., Clifton, D. A., Watkinson, P. J., Alastruey-Arimon, J., & Beale, R.(2017, Nov 22). Respiratory rate monitoring to detect deteriorations using wearable sensors.https://doi.org/10.5281/zenodo.1064813Citing this paperPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this maydiffer from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination,volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you areagain advised to check the publisher's website for any subsequent corrections.General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyrightowners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain•You may freely distribute the URL identifying the publication in the Research PortalTake down policyIf you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access tothe work immediately and investigate your claim.Download date: 07. Oct. 2023Results shown as bias ± limits of agreement [bpm]Respiratory rate monitoring to detect deteriorations using wearable sensorsP. H. Charlton 123, T. Bonnici 123, L. Tarassenko 2, D. A. Clifton 2, P. J. Watkinson 4, J. Alastruey 1, R. Beale 131 King’s College London;  2 University of Oxford; 3 Guy’s and St Thomas’ NHS Foundation Trust;  4 Oxford University HospitalsThis work was supported by the UK EPSRC (Grant EP/H019944/1), the NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London, the NIHR Oxford Biomedical Research Centre Programme, the Oxford and King's College London Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grant no. WT88877/Z/09/Z and grant no. WT088641/Z/09/Z, a Royal Academy of Engineering (RAEng) Research Fellowship awarded to DAC, and an EPSRC Challenge Award to DAC. The views expressed are those of the authors and not necessarily those of the EPSRC, NHS, NIHR, Department of Health, Wellcome Trust, or RAEng.Source:  Charlton, P.H., 2017. Continuous respiratory rate monitoring to detect clinical deteriorations using wearable sensors. PhD Thesis, King’s College London.1. Continuous respiratory rate (RR) monitoring using wearable sensors2. Development of an algorithm to estimate RR from the ECG3. Detection of deteriorations in real-timeContinuous monitoring may provide early warning of deteriorationsRespiratory rate (RR, number of breaths per minute) often increases in the hours before acute deteriorations such as cardiac arrests and sepsis. RR is currently measured by hand every 4 – 6 hours in hospitalised patients. Consequently, changes in RR can go unrecognised between measurements.It is difficult to monitor RR in ambulatory patientsContinuous RR monitoring often relies on cumbersome sensors, such as the chest band, facemask and oral-nasal cannula shown below. These are not suitable for monitoring ambulatory patients for several days.RR could be estimated from ECG or PPG signalsBoth the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals can be continuously monitored using wearable sensors.PPG ECGThe ECG and PPG are modulated by respirationThe ECG and PPG are influenced by respiration in three ways. This provides opportunity to estimate RR from the signals.OriginalBaseline ModulationAmplitude ModulationFrequency ModulationRR algorithms in the literatureA systematic review of the literature identified 140 publications containing evaluations of RR algorithms. A total of 95 candidate algorithms were implemented for testing.Publicly available datasetsFour datasets were identified with which to assess RR algorithms. Two datasets (Vortal and Fantasia) were acquired from healthy subjects, and two (MIMIC-II and CapnoBase) were collected from hospital patients.Assessment of RR algorithmsThe performances of RR algorithms were assessed on the four datasets using the limits of agreement statistics: the bias (i.e. mean error) and the limits of agreement (LoAs, within which 95% of errors are expected to lie). The results are shown in the table (bias ± LOAs in breaths per min).The best algorithm in the literature demonstrated high levels of inaccuracy. The high LoAs of ± 8.6 and ± 10.0 bpm on the MIMIC-II dataset showed that the algorithm was too imprecise for continuous monitoring.Refinement for continuous monitoringA novel RR algorithm was designed to provide more precise RR estimates (results in table). It achieved  lower LOAs of ± 3.2 bpm when using the ECG.Dataset Best algorithm in literature Novel RR algorithmECG PPG ECG PPGHealthy subjects     Vortal -1.8 ± 7.9 -5.0 ± 10.2 -0.2 ± 3.1 -0.9 ± 4.5Fantasia -1.8 ± 7.8 n / a -0.2 ± 2.5 n / aHospitalised patients     CapnoBase -0.1 ± 3.6 0.2 ± 4.9 0.4 ± 1.5 0.8 ± 3.3MIMIC-II -1.2 ± 8.6 -1.8 ± 10.0 0.0 ± 3.2 -0.2 ± 9.0Application to clinical data184 patients recovering from cardiac surgery in hospital were monitored using wearable sensors whilst staying on an ambulatory ward. The novel RR algorithm was used to estimate RRs from ECG signals. In addition, heart rate and blood oxygen saturation measurements were obtained from the wearable sensor.Detection of deteriorationsA continuous early warning score was calculated by fusing the three wearable sensor parameters with intermittent measurements of blood pressure, temperature and the use of supplementary oxygen. Deterioration alerts were generated when ≥ 80 % of scores in the previous 30 mins were elevated.Performance of continuous early warning scoresThe performances of continuous early warning scores for predicting whether there would be a clinical event in the next 48 hours were assessed. Clinical events were separated into adverse events (AEs) and severe AEs (SAEs).Upper plots: The AUROCs were similar when using continuous (wearable sensor) data, or manual intermittent data. This demonstrates the feasibility of using continuous RR estimates to calculate early warning scores.Lower plots: The false alert rate was greatly increased when using wearable sensor data. This was due to the increased (continuous) rate of wearable sensor measurements, compared to 4 – 6 hourly intermittent measurements.Further workThe false alert rate associated with continuous early warning scoring was too high. Therefore, further work is required to improve the performance of algorithms to detect deteriorations from wearable sensor data.AUROCs:0.7940.780AUROCs:0.6860.727Unrecognised changeSource: Charlton, Peter H. (2016, July). The Processes and Benefits of Sharing Clinical Data. Zenodo. http://doi.org/10.5281/zenodo.166546Source: Charlton, Peter H. (2016, July). The Processes and Benefits of Sharing Clinical Data. Zenodo. http://doi.org/10.5281/zenodo.166546Source: Peter H Charlton. (2016). Wireless Wrist Pulse Oximeter Photo. Zenodo. http://doi.org/10.5281/zenodo.569814Source: Charlton, P.H. et al., 2017. Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physiological Measurement, 38(5), pp.669–90. DOI: 10.1088/1361-6579/aa670e

Respiratory rate monitoring to detect deteriorations using wearable sensors

Peter H Charlton

Timothy Bonnici

Lionel Tarassenko

David A Clifton

Peter J Watkinson

Jordi Alastruey

Richard Beale

ZENODO

Results shown as bias ± limits of agreement [bpm]Respiratory rate monitoring to detect deteriorations using wearable sensorsP. H. Charlton 123, T. Bonnici 123, L. Tarassenko 2, D. A. Clifton 2, P. J. Watkinson 4, J. Alastruey 1, R. Beale 131 King’s College London;  2 University of Oxford; 3 Guy’s and St Thomas’ NHS Foundation Trust;  4 Oxford University HospitalsThis work was supported by the UK EPSRC (Grant EP/H019944/1), the NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London, the NIHR Oxford Biomedical Research Centre Programme, the Oxford and King's College London Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grant no. WT88877/Z/09/Z and grant no. WT088641/Z/09/Z, a Royal Academy of Engineering (RAEng) Research Fellowship awarded to DAC, and an EPSRC Challenge Award to DAC. The views expressed are those of the authors and not necessarily those of the EPSRC, NHS, NIHR, Department of Health, Wellcome Trust, or RAEng.Source:  Charlton, P.H., 2017. Continuous respiratory rate monitoring to detect clinical deteriorations using wearable sensors. PhD Thesis, King’s College London.1. Continuous respiratory rate (RR) monitoring using wearable sensors2. Development of an algorithm to estimate RR from the ECG3. Detection of deteriorations in real-timeContinuous monitoring may provide early warning of deteriorationsRespiratory rate (RR, number of breaths per minute) often increases in the hours before acute deteriorations such as cardiac arrests and sepsis. RR is currently measured by hand every 4 – 6 hours in hospitalised patients. Consequently, changes in RR can go unrecognised between measurements.It is difficult to monitor RR in ambulatory patientsContinuous RR monitoring often relies on cumbersome sensors, such as the chest band, facemask and oral-nasal cannula shown below. These are not suitable for monitoring ambulatory patients for several days.RR could be estimated from ECG or PPG signalsBoth the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals can be continuously monitored using wearable sensors.PPG ECGThe ECG and PPG are modulated by respirationThe ECG and PPG are influenced by respiration in three ways. This provides opportunity to estimate RR from the signals.OriginalBaseline ModulationAmplitude ModulationFrequency ModulationRR algorithms in the literatureA systematic review of the literature identified 140 publications containing evaluations of RR algorithms. A total of 95 candidate algorithms were implemented for testing.Publicly available datasetsFour datasets were identified with which to assess RR algorithms. Two datasets (Vortal and Fantasia) were acquired from healthy subjects, and two (MIMIC-II and CapnoBase) were collected from hospital patients.Assessment of RR algorithmsThe performances of RR algorithms were assessed on the four datasets using the limits of agreement statistics: the bias (i.e. mean error) and the limits of agreement (LoAs, within which 95% of errors are expected to lie). The results are shown in the table (bias ± LOAs in breaths per min).The best algorithm in the literature demonstrated high levels of inaccuracy. The high LoAs of ± 8.6 and ± 10.0 bpm on the MIMIC-II dataset showed that the algorithm was too imprecise for continuous monitoring.Refinement for continuous monitoringA novel RR algorithm was designed to provide more precise RR estimates (results in table). It achieved  lower LOAs of ± 3.2 bpm when using the ECG.Dataset Best algorithm in literature Novel RR algorithmECG PPG ECG PPGHealthy subjects     Vortal -1.8 ± 7.9 -5.0 ± 10.2 -0.2 ± 3.1 -0.9 ± 4.5Fantasia -1.8 ± 7.8 n / a -0.2 ± 2.5 n / aHospitalised patients     CapnoBase -0.1 ± 3.6 0.2 ± 4.9 0.4 ± 1.5 0.8 ± 3.3MIMIC-II -1.2 ± 8.6 -1.8 ± 10.0 0.0 ± 3.2 -0.2 ± 9.0Application to clinical data184 patients recovering from cardiac surgery in hospital were monitored using wearable sensors whilst staying on an ambulatory ward. The novel RR algorithm was used to estimate RRs from ECG signals. In addition, heart rate and blood oxygen saturation measurements were obtained from the wearable sensor.Detection of deteriorationsA continuous early warning score was calculated by fusing the three wearable sensor parameters with intermittent measurements of blood pressure, temperature and the use of supplementary oxygen. Deterioration alerts were generated when ≥ 80 % of scores in the previous 30 mins were elevated.Performance of continuous early warning scoresThe performances of continuous early warning scores for predicting whether there would be a clinical event in the next 48 hours were assessed. Clinical events were separated into adverse events (AEs) and severe AEs (SAEs).Upper plots: The AUROCs were similar when using continuous (wearable sensor) data, or manual intermittent data. This demonstrates the feasibility of using continuous RR estimates to calculate early warning scores.Lower plots: The false alert rate was greatly increased when using wearable sensor data. This was due to the increased (continuous) rate of wearable sensor measurements, compared to 4 – 6 hourly intermittent measurements.Further workThe false alert rate associated with continuous early warning scoring was too high. Therefore, further work is required to improve the performance of algorithms to detect deteriorations from wearable sensor data.AUROCs:0.7940.780AUROCs:0.6860.727Unrecognised changeSource: Charlton, Peter H. (2016, July). The Processes and Benefits of Sharing Clinical Data. Zenodo. http://doi.org/10.5281/zenodo.166546Source: Charlton, Peter H. (2016, July). The Processes and Benefits of Sharing Clinical Data. Zenodo. http://doi.org/10.5281/zenodo.166546Source: Peter H Charlton. (2016). Wireless Wrist Pulse Oximeter Photo. Zenodo. http://doi.org/10.5281/zenodo.569814Source: Charlton, P.H. et al., 2017. Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physiological Measurement, 38(5), pp.669–90. DOI: 10.1088/1361-6579/aa670e

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Respiratory rate monitoring to detect deteriorations using wearable sensors

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