• Liangyou Chen, Thomas McKenna, Andrew Reisner, and Jaques Reifman.
• Bioinformatics Cell, US Army Medical Research and Materiel Command, MCMR-ZB-T, Building 363, Miller Drive, Ft. Detrick, MD 21702, USA. lchen@bioanalysis.org
• Physiol Meas. 2006 Sep 1;27(9):797-816.

AbstractWe 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.

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