Data fusion for estimating respiratory rate from a single-lead ECG

Respiratory rate, an important antecedent of patient deterioration, is inadequately recorded by hospital staff, partially due to the absence of a reliable automated technique for measuring it. The ECG has been proposed by several authors in recent years as a source of reliable respiratory information. Most algorithms proposed use either respiratory sinus arrhythmia (RSA) or the R-peak amplitude (RPA) modulation of the ECG. In this paper, we propose a novel method for estimating respiratory rate from the ECG which fuses frequency information from the two methods. The method was evaluated on data from 40 young and elderly subjects and validated against a "gold standard" respiratory rate obtained from simultaneously recorded respiration data. The fusion method outperformed the RSA and RPA methods, giving a mean absolute error of 0.81 bpm for the young subject population and 0.84 bpm for the elderly, using 1-min windows of data. Unlike other algorithms, the technique does not underperform at the lower or higher respiratory rates. © 2012 Elsevier Ltd.

[1]  J Strackee,et al.  Hemodynamic fluctuations and baroreflex sensitivity in humans: a beat-to-beat model. , 1987, The American journal of physiology.

[2]  Ciara O'Brien,et al.  A comparison of algorithms for estimation of a respiratory signal from the surface electrocardiogram , 2007, Comput. Biol. Medicine.

[3]  J Pardey,et al.  A review of parametric modelling techniques for EEG analysis. , 1996, Medical engineering & physics.

[4]  D. Wakefield,et al.  Respiratory rate predicts cardiopulmonary arrest for internal medicine inpatients , 1993, Journal of General Internal Medicine.

[5]  L. Tarassenko,et al.  A randomised controlled trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients , 2006, Anaesthesia.

[6]  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.

[7]  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.

[8]  T. Hodgetts,et al.  The identification of risk factors for cardiac arrest and formulation of activation criteria to alert a medical emergency team. , 2002, Resuscitation.

[9]  J. Hogan.  Why don't nurses monitor the respiratory rates of patients? , 2006, British journal of nursing.

[10]  Heimo Ihalainen,et al.  Tutorial on Univariate Autoregressive Spectral Analysis , 2005, Journal of clinical monitoring and computing.

[11]  D. Cysarz,et al.  Comparison of Respiratory Rates Derived from Heart Rate Variability, ECG Amplitude, and Nasal/Oral Airflow , 2008, Annals of Biomedical Engineering.

[12]  K. Hillman,et al.  Respiratory rate: the neglected vital sign , 2008, The Medical journal of Australia.

[13]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[14]  Lionel Tarassenko,et al.  Quantitative assessment of respiratory derivation algorithms , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

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

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

[18]  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.

[19]  K. Hillman,et al.  A comparison of Antecedents to Cardiac Arrests, Deaths and EMergency Intensive care Admissions in Australia and New Zealand, and the United Kingdom—the ACADEMIA study , 2004 .

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

[21]  K. Hillman,et al.  The objective medical emergency team activation criteria: a case-control study. , 2007, Resuscitation.

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

[23]  T. Rea,et al.  Prediction of critical illness during out-of-hospital emergency care. , 2010, JAMA.

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

[25]  V. Larsen,et al.  Impedance pneumography for long-term monitoring of respiration during sleep in adult males. , 1984, Clinical physiology.

[26]  H. V. van Geijn,et al.  Heart Rate Variability , 1993, Annals of Internal Medicine.

[27]  Yeon-Sik Noh,et al.  An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation , 2008, Medical & Biological Engineering & Computing.

[28]  A. McGinley,et al.  A physiologically‐based early warning score for ward patients: the association between score and outcome * , 2005, Anaesthesia.

[29]  C. Peng,et al.  Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. , 1996, The American journal of physiology.