Results shown as bias ± limits of agreement [bpm] Respiratory rate monitoring to detect deteriorations using wearable sensors P. H. Charlton 123, T. Bonnici 123, L. Tarassenko 2, D. A. Clifton 2, P. J. Watkinson 4, J. Alastruey 1, R. Beale 13 1 King’s College London; 2 University of Oxford; 3 Guy’s and St Thomas’ NHS Foundation Trust; 4 Oxford University Hospitals This 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 sensors 2. Development of an algorithm to estimate RR from the ECG 3. Detection of deteriorations in real-time Continuous monitoring may provide early warning of deteriorations Respiratory 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 patients Continuous 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 signals Both the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals can be continuously monitored using wearable sensors. PPG ECG The ECG and PPG are modulated by respiration The ECG and PPG are influenced by respiration in three ways. This provides opportunity to estimate RR from the signals. Original Baseline Modulation Amplitude Modulation Frequency Modulation RR algorithms in the literature A 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 datasets Four 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 algorithms The 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 monitoring A 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 algorithm ECG PPG ECG PPG Healthy subjects Vortal -1.8 ± 7.9 -5.0 ± 10.2 -0.2 ± 3.1 -0.9 ± 4.5 Fantasia -1.8 ± 7.8 n / a -0.2 ± 2.5 n / a Hospitalised patients CapnoBase -0.1 ± 3.6 0.2 ± 4.9 0.4 ± 1.5 0.8 ± 3.3 MIMIC-II -1.2 ± 8.6 -1.8 ± 10.0 0.0 ± 3.2 -0.2 ± 9.0 Application to clinical data 184 patients recovering from cardiac surgery in hospital were monitored using