Abstract
Respiratory rate (RR) estimation algorithms based on the photoplethymogram (PPG) and electrocardiogram (ECG) lack clinical robustness. This is because the PPG and ECG respiratory modulations are dependent on patient physiology, regardless of general signal quality. The present work describes an RR estimation algorithm using respiratory quality indices (RQIs) that assess the presence or absence of the PPG- and ECG-derived respiratory modulations. Six respiratory waveforms are derived from the amplitude modulation, frequency modulation, and baseline wander of the PPG and ECG. The respiratory quality of each modulation is assessed by using RQIs based on the fast Fourier transform, autoregression, and autocorrelation. The individual RQIs are fused to obtain a single RQI per modulation per time window. Based on a tunable threshold, the RQIs are used to discard poor modulations and weight the remaining modulations to provide a single RR estimation per time window. The proposed method was tested on two independent datasets and found that using a conservative threshold, the mean absolute error was 0.71 $\pm$ 0.89 and 3.12 $\pm$ 4.39 brpm while discarding only 1.3% and 23.2% of all time windows, for each dataset, respectively. These errors are either better than or comparable to current methods, and the number of windows discarded is far lower demonstrating improved robustness. This work describes a novel preprocessing algorithm that can be implemented in conjunction with other RR estimation techniques to improve robustness by specifically considering the quality of the respiratory information.
Highlights
A BNORMAL respiratory rate (RR) is one of the earliest and most prevalent indicators of in-hospital catastrophic deterioration
The value for E50 for all QS,k in Dc,1 and Dm exceeds 1 indicating that when 50% of the respiratory modulations are discarded, the average error in the RR estimate for all modulations decreases by at least 1 brpm
The present work describes the development and application of respiratory quality indices (RQIs), a novel pre-processing step in RR estimation that can quantify the level of confidence in the quality of extracted
Summary
A BNORMAL respiratory rate (RR) is one of the earliest and most prevalent indicators of in-hospital catastrophic deterioration. Abnormal RR preempts adverse events such as cardiac and respiratory arrest, systemic inflammatory response syndrome (SIRS), renal failure, transfer to the intensive care unit (ICU), and in-hospital mortality [1]–[5]. In a study of abnormal RR, it was found that 54% of all cardiac arrest patients in an internal medicine unit had RR > 27 brpm during at least one recording in the three days prior to cardiac arrest [2]. Despite the importance of measuring RR in identifying patient deterioration, RR has historically been the least monitored and recorded vital sign [5], [7], [8]. One of the primary causes for this is that, unlike other vital signs, the measurement of RR is performed manually in most settings because there is no widely available automated device to measure RR with suitable clinical robustness [4]–[7]
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