Probabilistic estimation of respiratory rate using Gaussian processes

The presence of respiratory information within the electrocardiogram (ECG) signal is a well-documented phenomenon. We present a Gaussian process framework for the estimation of respiratory rate from the different sources of modulation in a single-lead ECG. We propose a periodic covariance function to model the frequency- and amplitude-modulation time series derived from the ECG, where the hyperparameters of the process are used to derive the respiratory rate. The approach is evaluated using data taken from 40 healthy subjects each with 2 hours of monitoring, containing ECG and respiration waveforms. Results indicate that the accuracy of our proposed method is comparable with that of existing methods, but with the advantages of a principled probabilistic approach, including the direct quantification of the uncertainty in the estimation.

[1]  Ki H. Chon,et al.  Estimation of Respiratory Rate From Photoplethysmogram Data Using Time–Frequency Spectral Estimation , 2009, IEEE Transactions on Biomedical Engineering.

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

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

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

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

[6]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

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

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

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

[10]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[11]  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 .

[12]  J. N. Watson,et al.  Measurement Of Respiratory Rate From the Photoplethysmogram In Chest Clinic Patients , 2007, Journal of clinical monitoring and computing.

[13]  D. Harrison,et al.  Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward , 2007, Intensive Care Medicine.

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

[15]  J. Lee,et al.  Respiratory Rate Extraction Via an Autoregressive Model Using the Optimal Parameter Search Criterion , 2010, Annals of Biomedical Engineering.

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

[17]  H. Rue,et al.  Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .

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