A computer-implemented method of obtaining information about a respiratory rate from a time series of measurements obtained from a patient, comprising: obtaining the time series of measurements, the time series of measurements comprising RPA and RSA waveforms obtained from an ECG signal; and using Gaussian regression to fit a Gaussian process to each of the RPA and RSA waveforms, the Gaussian process being a stochastic process that expresses a dependent variable in terms of an independent variable via a latent function which can be interpreted as being a probability distribution over functions, the Gaussian process being defined by a mean function and a periodic covariance function, wherein: for each of the RPA and RSA waveforms the fitting of the Gaussian process is used to obtain an estimate of the respiratory rate and an estimate of the uncertainty in the respiratory rate directly from the distribution over the hyperparameter defining the period of the covariance function, the hyperparameter being equal or directly proportional to the period of breathing corresponding to the respiratory rate, each estimate of the respiratory rate is obtained from the estimated mode of the distribution over the hyperparameter defining the period of the covariance function, each estimate of the uncertainty in the estimated respiratory rate is obtained from the variance of the distribution over the hyperparameter defining the period of the covariance function at the mode, and the method further comprises selecting and outputting the estimate of the respiratory rate for which the variance of the distribution is lowest as the estimate of the respiratory rate having the lower uncertainty. obtaining the time series of measurements, the time series of measurements comprising RPA and RSA waveforms obtained from an ECG signal; and using Gaussian regression to fit a Gaussian process to each of the RPA and RSA waveforms, the Gaussian process being a stochastic process that expresses a dependent variable in terms of an independent variable via a latent function which can be interpreted as being a probability distribution over functions, the Gaussian process being defined by a mean function and a periodic covariance function, wherein: for each of the RPA and RSA waveforms the fitting of the Gaussian process is used to obtain an estimate of the respiratory rate and an estimate of the uncertainty in the respiratory rate directly from the distribution over the hyperparameter defining the period of the covariance function, the hyperparameter being equal or directly proportional to the period of breathing corresponding to the respiratory rate, each estimate of the respiratory rate is obtained from the estimated mode of the distribution over the hyperparameter defining the period of the covariance function, each estimate of the uncertainty in the estimated respiratory rate is obtained from the variance of the distribution over the hyperparameter defining the period of the covariance function at the mode, and the method further comprises selecting and outputting the estimate of the respiratory rate for which the variance of the distribution is lowest as the estimate of the respiratory rate having the lower uncertainty. for each of the RPA and RSA waveforms the fitting of the Gaussian process is used to obtain an estimate of the respiratory rate and an estimate of the uncertainty in the respiratory rate directly from the distribution over the hyperparameter defining the period of the covariance function, the hyperparameter being equal or directly proportional to the period of breathing corresponding to the respiratory rate, each estimate of the respiratory rate is obtained from the estimated mode of the distribution over the hyperparameter defining the period of the covariance function, each estimate of the uncertainty in the estimated respiratory rate is obtained from the variance of the distribution over the hyperparameter defining the period of the covariance function at the mode, and the method further comprises selecting and outputting the estimate of the respiratory rate for which the variance of the distribution is lowest as the estimate of the respiratory rate having the lower uncertainty.