Respiratory Quality Index Design and Validation for ECG and PPG Derived Respiratory Data

Patient vital signs have been widely recognized as an early indicator of catastrophic patient decline in the hospital. This has led to the widespread implementation of Rapid Response Teams (RRTs) and Early Warning Score (EWS) systems within hospitals. However, in order to both RRTs and EWS systems to be most effective, it is critical that patient vital signs are recorded accurately and often. However, despite findings that of the four major vital signs respiratory rate is the most crucial in predicting catastrophic decline, it is the most often not recorded. One possible solution to this is to develop a device that is capable of non-invasively monitoring respiratory rate. Two prime candidates for this are the photoplethymsograph and electrocardiogram. Both the PPG and ECG are widely used to measure heart rate in the hospital and because the heart rate is modulated in multiple ways by the respiratory rate, it is possible to extract a respiratory rate estimate from both the PPG and ECG. However, the overall modulations of the respiratory rate on the PPG and ECG are small meaning that often algorithms that are designed to detect them cannot tell when these modulations are actually present or not. This reasearch presents a method for detecting when the respiratory modulations can be extracted from the PPG and ECG using respiratory quality indices (RQIs) which are implemented after extraction of the respiratory rate modulations but before the respiratory rate is estimated. This research was performed using three different datasets of varying data quality. The first dataset was the CapnoBase dataset which contains PPG, ECG, and capnography for anesthetized patients. This dataset is considered the “gold standard” for comparison. The second dataset was the MIMIC II dataset which contains PPG, ECG, and transthoracic impedance (TTI) data for patients that are bedridden in the hospital. This was considered a “silver standard” for comparison. Finally, the last dataset was the PICRAM dataset which was collected for mobile patients and contained PPG and ECG waveforms in addition to clinician annotations for respiratory rate. This was considered the “bronze standard” for comparison. These datasets were used to test and validate four different RQIs: the FFT-RQI, the ARRQI, the AutoCor-RQI, and the Hjorth Parameter RQI. The FFT-RQI is based on a signals Fast Fourier Transform and functions by calculating the area of the largest peak in the frequency domain compared to the rest of the signal. The AR-RQI works based on the autocorrelation function. The AR-RQI is calculated by finding the best performing AR model order and selecting the largest pole for that model order to represent the AR-RQI. The AutoCor-RQI is based on the autocorrelation fuction and seeks to find the area within the respiratory rate range where the signal best corresponds to itself where the higher the autocorrelation value is, the more likely it is to correspond to an actual respiratory wave. Finally, the Hjorth Parameter RQI is calculated using the third Hjorth parameter which is a measure of how sinusoidal a signal where the periodicity of a signal is expected to correspond to a more pronounced respiratory waveform. The results of this research ultimately demonstrated that three of the four RQIs: the FFT-RQI, AR-RQI, and AutoCor-RQI, perform better than a signal quality index (SQI) applied prior to respiratory rate modulation extraction. Additionally, it was found that in general the RQIs were robust and performed well when different respiratory rate estimation algorithms were applied as the standard of comparison, when the data quality was reduced from CapnoBase to PICRAM, and for both the PPG and ECG signals. While this research demonstrates a promising future role for RQIs in respiratory rate estimation from PPG and ECG data, further work is still needed in order to fuse the different RQIs to get a single respiratory rate estimate. Ultimately however, this work shows a lot of promise for creating a robust respiratory rate estimation algorithm that can be used in both hospital and clinical settings in the developing and developed world.