Robust estimation of respiratory rate via ECG- and PPG-derived respiratory quality indices

Respiratory rate (RR) is one of the most informative indicators of a patient's health status. However, automated, non-invasive measurements of RR are insufficiently robust for use in clinical practice. A number of methods have been described in the literature to estimate RR from both photo-plethysmography (PPG) and electrocardiography (ECG) based on three physiological modulations of respiration: amplitude modulation (AM), frequency modulation (FM), and baseline wander (BW). However, the quality of the respiratory information acquired is highly patient-dependent and often too noisy to be used. We address this by proposing respiratory quality indices (RQIs) that quantify the quality of the respiratory signal that can be extracted from each modulation from both PPG and ECG waveforms. Signal quality indices (SQIs) detect artefact in the ECG and PPG, which is relatively straight-forward. RQIs have a different role: they quantify if an individual patient's physiology is modulating the sensor waveforms. We have designed four RQIs based on Fourier transform (RQI
_FFT
), autocorrelation (RQI
_AC
), autoregression (RQI
_AR
), and Hjorth complexity (RQI
_HC
). We validated the approach using PPG and ECG data in the CapnoBase and MIMIC II datasets. We conclude that the novel implementation of an RQI-based preprocessing step has the potential to improve substantially the performance of RR estimation algorithms.