Analysis Of Respiratory Variability Research Articles

Segmentation and classification of capnograms: application in respiratory variability analysis

Variability analysis of respiratory waveforms has been shown to provide key insights into respiratory physiology and has been used successfully to predict clinical outcomes. The current standard for quality assessment of the capnogram signal relies on a visual analysis performed by an expert in order to identify waveform artifacts. Automated processing of capnograms is desirable in order to extract clinically useful features over extended periods of time in a patient monitoring environment. However, the proper interpretation of capnogram derived features depends upon the quality of the underlying waveform. In addition, the comparison of capnogram datasets across studies requires a more practical approach than a visual analysis and selection of high-quality breath data. This paper describes a system that automatically extracts breath-by-breath features from capnograms and estimates the quality of individual breaths derived from them. Segmented capnogram breaths were presented to expert annotators, who labeled the individual physiological breaths into normal and multiple abnormal breath types. All abnormal breath types were aggregated into the abnormal class for the purpose of this manuscript, with respiratory variability analysis as the end-application. A database of 11 526 breaths from over 300 patients was created, comprising around 35% abnormal breaths. Several simple classifiers were trained through a stratified repeated ten-fold cross-validation and tested on an unseen portion of the labeled breath database, using a subset of 15 features derived from each breath curve. Decision Tree, K-Nearest Neighbors (KNN) and Naive Bayes classifiers were close in terms of performance (AUC of 90%, 89% and 88% respectively), while using 7, 4 and 5 breath features, respectively. When compared to airflow derived timings, the 95% confidence interval on the mean difference in interbreath intervals was ± 0.18 s. This breath classification system provides a fast and robust pre-processing of continuous respiratory waveforms, thereby ensuring reliable variability analysis of breath-by-breath parameter time series.

Influence of chemoreflexes on respiratory variability in healthy subjects.

The background of this study was the hypothesis that respiratory variability is influenced by chemoreflex regulation. In search for periodicities in the variability due to instability of the respiratory control system, spectral analysis was applied to breath-to-breath variables in 19 healthy subjects at rest. During room-air breathing, coherent oscillations in end-tidal CO2 (PET(CO2)) and mean inspiratory flow (VI/TI) were found in 15 subjects with frequencies mostly below 0.15 cycles per breath. Coherent oscillations in PET(CO2) and VI/TI were expressed by gain (0.13 to 0.34 L/second small middle dot kPa) and phase (-170 degrees to +8 degrees ). The oscillations in VI/TI were in phase with inspiratory volume (VI). A model that describes the effects of chemoreflex feedback to noise in the system could explain these gains and phases, whereas a model without chemoreflex could not. During 100% O2 breathing, only eight subjects had coherent oscillations in PET(CO2) and VI/TI. The coherent oscillations in PET(CO2) and VI/TI were interpreted as a manifestation of chemoreflex activity. We conclude that respiratory variability is not a random process but contains information on chemoreflex properties, such as the chemoreflex gain. The analysis of respiratory variability therefore provides a new tool to study the action of the chemoreflexes without application of external stimuli.