Algorithms to qualify respiratory data collected during the transport of trauma patients

Abstract

We developed a quality indexing system to numerically qualify respiratory data collected by vital-sign monitors in order to support reliable post-hoc mining of respiratory data. Each monitor-provided (reference) respiratory rate (RR R ) is evaluated, second-by-second, to quantify the reliability of the rate with a quality index (QI R ). The quality index is calculated from: (1) a breath identification algorithm that identifies breaths of 'typical' sizes and recalculates the respiratory rate (RR C ); (2) an evaluation of the respiratory waveform quality (QI W ) by assessing waveform ambiguities as they impact the calculation of respiratory rates and (3) decision rules that assign a QI R based on RR R , RR C and QI W . RR C , QI W and QI R were compared to rates and quality indices independently determined by human experts, with the human measures used as the 'gold standard', for 163 randomly chosen 15 s respiratory waveform samples from our database. The RR C more closely matches the rates determined by human evaluation of the waveforms than does the RR R (difference of 3.2 ± 4.6 breaths min −1 versus 14.3 ± 19.3 breaths min −1 , mean ± STD, p < 0.05). Higher QI W is found to be associated with smaller differences between calculated and human-evaluated rates (average differences of 1.7 and 8.1 breaths min −1 for the best and worst QI W , respectively). Establishment of QI W and QI R , which ranges from 0 for the worst-quality data to 3 for the best, provides a succinct quantitative measure that allows for automatic and systematic selection of respiratory waveforms and rates based on their data quality.

Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

“…Chen et al [3] developed a method to estimate respiratory waveform quality based on breath detection over varying baseline values. Nemati et al [25] proposed using the spectral purity as the respiratory signal quality index.…”

Prehospital Heart Rate and Blood Pressure Increase the Positive Predictive Value of the Glasgow Coma Scale for High-Mortality Traumatic Brain Injury

“…20,24,25 Here, we used the same validated methodology, summarized as follows: For each vital-sign value, reliable data were identified by automated algorithms that rated each datum on an integer scale from least to most reliable. 24,26,27 HR and RR reliability algorithms involved analysis of ECG and IP waveforms, respectively. 26,27 Briefly, when the waveforms were clean with rhythmic, consistent beats or breaths, the corresponding rates tended to be rated as reliable.…”

“…24,26,27 HR and RR reliability algorithms involved analysis of ECG and IP waveforms, respectively. 26,27 Briefly, when the waveforms were clean with rhythmic, consistent beats or breaths, the corresponding rates tended to be rated as reliable. Conversely, when the waveforms were noisy with irregular, heterogeneous beats or breaths, the rates were rated as unreliable.…”

Automatically-Computed Prehospital Severity Scores are Equivalent to Scores Based on Medic Documentation

“…Recent reports described a set of signal quality indices ("SQI's"), 15,16 which are computer algorithms that rate the reliability of vital signs measurements made by a Propaq (Welch Allyn, Beaverton, OR) transport monitor (blood pressure, respiratory rate [RR], heart rate [HR], and oxygen saturation 17 ). We tested if SQI's would improve the predictive power of automatically-computed severity scores calculated directly from Propaq data by eliminating unreliable vital signs.…”