An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram

Abstract Over 100 algorithms have been proposed to estimate respiratory rate (RR) from the electrocardiogram (ECG) and photoplethysmogram (PPG). As they have never been compared systematically it is unclear which algorithm performs the best. Our primary aim was to determine how closely algorithms agreed with a gold standard RR measure when operating under ideal conditions. Secondary aims were: (i) to compare algorithm performance with IP, the clinical standard for continuous respiratory rate measurement in spontaneously breathing patients; (ii) to compare algorithm performance when using ECG and PPG; and (iii) to provide a toolbox of algorithms and data to allow future researchers to conduct reproducible comparisons of algorithms. Algorithms were divided into three stages: extraction of respiratory signals, estimation of RR, and fusion of estimates. Several interchangeable techniques were implemented for each stage. Algorithms were assembled using all possible combinations of techniques, many of which were novel. After verification on simulated data, algorithms were tested on data from healthy participants. RRs derived from ECG, PPG and IP were compared to reference RRs obtained using a nasal-oral pressure sensor using the limits of agreement (LOA) technique. 314 algorithms were assessed. Of these, 270 could operate on either ECG or PPG, and 44 on only ECG. The best algorithm had 95% LOAs of  −4.7 to 4.7 bpm and a bias of 0.0 bpm when using the ECG, and  −5.1 to 7.2 bpm and 1.0 bpm when using PPG. IP had 95% LOAs of  −5.6 to 5.2 bpm and a bias of  −0.2 bpm. Four algorithms operating on ECG performed better than IP. All high-performing algorithms consisted of novel combinations of time domain RR estimation and modulation fusion techniques. Algorithms performed better when using ECG than PPG. The toolbox of algorithms and data used in this study are publicly available.

[1]  V. Wright,et al.  An objective clinical study of chest expansion. , 1972, Annals of the rheumatic diseases.

[2]  J. Hirsch,et al.  Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. , 1981, The American journal of physiology.

[3]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[4]  I. O'Brien,et al.  Heart rate variability in healthy subjects: effect of age and the derivation of normal ranges for tests of autonomic function. , 1986, British heart journal.

[5]  Willis J. Tompkins,et al.  Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database , 1986, IEEE Transactions on Biomedical Engineering.

[6]  W. Wieling.  Heart rate variability in healthy subjects: effect of age and derivation of normal ranges for tests of autonomic function. , 1987, British heart journal.

[7]  Hurst Jw,et al.  Respiratory Rate and Pattern -- Clinical Methods: The History, Physical, and Laboratory Examinations , 1990 .

[8]  S. Braun.  Respiratory Rate and Pattern , 1990 .

[9]  C. Sprung,et al.  Clinical antecedents to in-hospital cardiopulmonary arrest. , 1990, Chest.

[10]  E. Antman,et al.  ACC/AHA 2002 guideline update for exercise testing: summary article: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Update the 1997 Exercise Testing Guidelines). , 2002, Circulation.

[11]  D. Mark,et al.  ACC/AHA Guidelines for Exercise Testing. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Exercise Testing). , 1997, Journal of the American College of Cardiology.

[12]  D. Goldhill,et al.  Physiological values and procedures in the 24 h before ICU admission from the ward , 1999, Anaesthesia.

[13]  J. Thayer,et al.  Estimating respiratory frequency from autoregressive spectral analysis of heart period. , 2002, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[14]  Pablo Laguna,et al.  Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal , 2003, IEEE Transactions on Biomedical Engineering.

[15]  Paul S. Addison,et al.  Secondary Transform Decoupling of Shifted nonstationary Signal Modulation Components: Application to Photoplethysmography , 2004, Int. J. Wavelets Multiresolution Inf. Process..

[16]  T. Strömberg,et al.  Influence of Tidal Volume and THoraco-abdominal Separation on the Respiratory Induced Variation of the Photoplethysmogram , 2004, Journal of clinical monitoring and computing.

[17]  S. Ridley.  The recognition and early management of critical illness. , 2005, Annals of the Royal College of Surgeons of England.

[18]  Patrick E. McSharry,et al.  Advanced Methods And Tools for ECG Data Analysis , 2006 .

[19]  P. Öberg,et al.  Monitoring of respiratory and heart rates using a fibre-optic sensor , 1992, Medical and Biological Engineering and Computing.

[20]  A. Johansson.  Neural network for photoplethysmographic respiratory rate monitoring , 2003, Medical and Biological Engineering and Computing.

[21]  H. Barnhart,et al.  An Overview on Assessing Agreement with Continuous Measurements , 2007, Journal of biopharmaceutical statistics.

[22]  Susannah G. Fleming Lionel Tarassenko.  A Comparison of Signal Processing Techniques for the Extraction of Breathing Rate from the Photoplethysmogram , 2007 .

[23]  Axel Schäfer,et al.  Estimation of Breathing Rate from Respiratory Sinus Arrhythmia: Comparison of Various Methods , 2008, Annals of Biomedical Engineering.

[24]  George B. Moody,et al.  Derivation of Respiratory Signals from Multi-lead ECGs , 2008 .

[25]  B. Carstensen,et al.  Statistical Models for Assessing Agreement in Method Comparison Studies with Replicate Measurements , 2008, The international journal of biostatistics.

[26]  L. Tarassenko,et al.  Non-invasive measurement of respiratory rate in children using the photoplethysmogram , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Yang Hao,et al.  Wireless body sensor networks for health-monitoring applications , 2008, Physiological measurement.

[28]  L. Tarassenko,et al.  Spectral fusion for estimating respiratory rate from the ECG , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[29]  L. Tarassenko,et al.  Telemetry-based vital sign monitoring for ambulatory hospital patients , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  Sung-Bock Kim,et al.  Wearable Respiratory Rate Monitoring using Piezo-resistive Fabric Sensor , 2009 .

[31]  Ki H. Chon,et al.  An Autoregressive Model-Based Particle Filtering Algorithms for Extraction of Respiratory Rates as High as 90 Breaths Per Minute From Pulse Oximeter , 2010, IEEE Transactions on Biomedical Engineering.

[32]  I. Romero,et al.  Evaluation of methods for estimation of respiratory frequency from the ECG , 2010, 2010 Computing in Cardiology.

[33]  Shamim Nemati,et al.  Data Fusion for Improved Respiration Rate Estimation , 2010, EURASIP J. Adv. Signal Process..

[34]  Mark F. Bocko,et al.  Methods to extract respiration information from ECG signals , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[35]  Paolo Bonato,et al.  Wearable Sensors and Systems , 2010, IEEE Engineering in Medicine and Biology Magazine.

[36]  S. Fleming.  Measurement and fusion of non-invasive vital signs for routine triage of acute paediatric illness , 2010 .

[37]  Yang Hao,et al.  Detecting Vital Signs with Wearable Wireless Sensors , 2010, Sensors.

[38]  J. N. Watson,et al.  Developing an algorithm for pulse oximetry derived respiratory rate (RRoxi): a healthy volunteer study , 2012, Journal of Clinical Monitoring and Computing.

[39]  Jesús Lázaro,et al.  Deriving respiration from photoplethysmographic pulse width , 2013, Medical & Biological Engineering & Computing.

[40]  L. Tarassenko.  Vital sign monitoring and data fusion for paediatric triage , 2012 .

[41]  Sabine Van Huffel,et al.  Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration , 2012, IEEE Transactions on Biomedical Engineering.

[42]  Walter Karlen,et al.  Adaptive pulse segmentation and artifact detection in photoplethysmography for mobile applications , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[43]  L. Tarassenko,et al.  Photoplethysmographic derivation of respiratory rate: a review of relevant physiology , 2012, Journal of medical engineering & technology.

[44]  Lionel Tarassenko,et al.  Data fusion for estimating respiratory rate from a single-lead ECG , 2013, Biomed. Signal Process. Control..

[45]  Walter Karlen,et al.  Multiparameter Respiratory Rate Estimation From the Photoplethysmogram , 2013, IEEE Transactions on Biomedical Engineering.

[46]  K.Rajkumar K.Ramya.  Respiration Rate Diagnosis Using Single Lead ECG in Real Time , 2013 .

[47]  K. Ramya,et al.  Respiration Rate Diagnosis Using Single Lead ECG in Real Time , 2013 .

[48]  L. Masotti,et al.  Respiratory rate assessments using a dual-accelerometer device , 2014, Respiratory Physiology & Neurobiology.

[49]  J. N. Watson,et al.  Pulse oximetry-derived respiratory rate in general care floor patients , 2014, Journal of Clinical Monitoring and Computing.

[50]  Jonathan B. Clark,et al.  Physiological monitoring and analysis of a manned stratospheric balloon test program. , 2014, Aviation, space, and environmental medicine.

[51]  W. Karlen,et al.  Estimating Respiratory and Heart Rates from the Correntropy Spectral Density of the Photoplethysmogram , 2014, PloS one.

[52]  David A. Clifton,et al.  Probabilistic Estimation of Respiratory Rate from Wearable Sensors , 2015 .

[53]  L. Tarassenko,et al.  Respiratory rate estimation during triage of children in hospitals , 2015, Journal of medical engineering & technology.

[54]  Pablo Laguna Lasaosa,et al.  Non-invasive techniques for respiratory information extraction based on pulse photoplethysmogram and electrocardiogram , 2015 .

[55]  David A. Clifton,et al.  Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring , 2015, IEEE Journal of Biomedical and Health Informatics.

[56]  U. Koehler,et al.  [Respiratory Rate - a Neglected Vital Sign]. , 2017, Deutsche medizinische Wochenschrift.