Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters.

GOAL: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. METHODS: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. RESULTS: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25[Formula: see text]-75[Formula: see text] percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). CONCLUSION: Increased robustness of RR estimation by the proposed method was demonstrated. SIGNIFICANCE: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

Respiratory quality indices for automated monitoring of respiration from sensor data

Abnormal respiratory rate (RR) is known to be one of the most clinically effective predictors of catastrophic decline. Despite this, RR is often the least monitored and most inaccurately measured vital sign. This is primarily because of the lack of a non-invasive, robust, automated method for estimating RR. It has previously been shown that the amplitude modulation (AM), frequency modulation (FM), and baseline wander (BW) of both the electrocardiogram (ECG) and photoplethysmogram (PPG) contain respiratory waveform information. However, these respiratory modulations are driven by the physiologic interrelationship between the cardiovascular and respiratory systems and may or may not be present based on a patient's characteristics and condition. Despite this, current methods for RR estimation from ECG and PPG do not account for this physiologic variability. The investigations in this thesis describe the development and evaluation of respiratory quality indices (RQIs), a novel method for evaluating the presence or absence of physiologically important respiratory information from the AM, FM, and BW extracted from the ECG and PPG. This work is conducted in three unique data sets, CapnoBase, MIMIC-III, and Dialysis III, all of which represent important, different patient populations. Five initial RQIs are described based on five signal processing techniques: fast Fourier transform (FFT), autocorrelation, cosine correlation, autoregression, and Hjorth parameters. Of these, the individual RQIs based on the FFT, autocorrelation, and autoregression are deemed to be good predictors of the presence of respiratory waveform data. The three individual RQIs are used to derive three fusion RQIs based on two supervised learning algorithms: linear regression and support vector regression (SVR) and one unsupervised learning algorithm: principal component analysis (PCA). Both the linear regression and PCA fusion RQIs are accurate and robust. The linear regression fusion RQI is used in the development of an RR estimation algorithm, termed RQIFusion, which achieves highly accurate and more complete RR estimates than existing methods. In the Dialysis III data set, implementation of RQIFusion improved RR estimation error by between 1.35 to 2.29 breaths per minute (brpm) to achieve RR estimation errors between 2.18 to 3.46 brpm, depending on the RR estimation algorithm employed. These results represent a marked improvement in RR estimation and indicate the importance of conducting respiratory quality analysis using RQIs on respiratory modulations extracted from ECG and PPG prior to RR estimation.</p

Respiratory quality indices for automated monitoring of respiration from sensor data

Abnormal respiratory rate (RR) is known to be one of the most clinically effective predictors of catastrophic decline. Despite this, RR is often the least monitored and most inaccurately measured vital sign. This is primarily because of the lack of a non-invasive, robust, automated method for estimating RR. It has previously been shown that the amplitude modulation (AM), frequency modulation (FM), and baseline wander (BW) of both the electrocardiogram (ECG) and photoplethysmogram (PPG) contain respiratory waveform information. However, these respiratory modulations are driven by the physiologic interrelationship between the cardiovascular and respiratory systems and may or may not be present based on a patient's characteristics and condition. Despite this, current methods for RR estimation from ECG and PPG do not account for this physiologic variability. The investigations in this thesis describe the development and evaluation of respiratory quality indices (RQIs), a novel method for evaluating the presence or absence of physiologically important respiratory information from the AM, FM, and BW extracted from the ECG and PPG. This work is conducted in three unique data sets, CapnoBase, MIMIC-III, and Dialysis III, all of which represent important, different patient populations. Five initial RQIs are described based on five signal processing techniques: fast Fourier transform (FFT), autocorrelation, cosine correlation, autoregression, and Hjorth parameters. Of these, the individual RQIs based on the FFT, autocorrelation, and autoregression are deemed to be good predictors of the presence of respiratory waveform data. The three individual RQIs are used to derive three fusion RQIs based on two supervised learning algorithms: linear regression and support vector regression (SVR) and one unsupervised learning algorithm: principal component analysis (PCA). Both the linear regression and PCA fusion RQIs are accurate and robust. The linear regression fusion RQI is used in the development of an RR estimation algorithm, termed RQIFusion, which achieves highly accurate and more complete RR estimates than existing methods. In the Dialysis III data set, implementation of RQIFusion improved RR estimation error by between 1.35 to 2.29 breaths per minute (brpm) to achieve RR estimation errors between 2.18 to 3.46 brpm, depending on the RR estimation algorithm employed. These results represent a marked improvement in RR estimation and indicate the importance of conducting respiratory quality analysis using RQIs on respiratory modulations extracted from ECG and PPG prior to RR estimation.</p

Development and validation of early warning score systems for COVID‐19 patients

COVID‐19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high‐flow nasal oxygen, continuous positive airways pressure, non‐invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub‐optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests