Multivariable Regression Model to Estimate Tidal Volume for Different Respiratory Patterns.

Respiratory patterns present great variability, both in healthy subjects and in patients with different diseases and forms of nasal, oral, superficial or deep breathing. The analysis of this variability depends, among others, on the device used to record the signals that describe these patterns. In this study, we propose multivariable regression models to estimate tidal volume (VT) considering different breathing patterns. Twenty-three healthy volunteers underwent continuous multisensor recordings considering different modes of breathing. Respiratory flow and volume signals were recorded with a pneumotachograph and thoracic and abdominal respiratory inductive plethysmographic bands. Several respiratory parameters were extracted from the volume signals, such as inspiratory and expiratory areas (Areains, Areaexp), maximum volume relative to the cycle start and end (VTins, VTexp), inspiratory and expiratory time (Tins, Texp), cycle duration (Ttot), and normalized parameters of clinical interest. The parameters with the greatest individual predictive power were combined using multivariable models to estimate VT. Their performance were quantified in terms of determination coefficient (R2), relative error (ER) and interquartile range (IQR). Using only three parameters, the results obtained for the thoracic band (VTexp, Ttot, Areaexp) were better than those obtained from the abdominal band (VTexp, Tins, Areains) with R2 = 0.94 (IQR: 0.07); ER = 6.99 (IQR: 6.12) vs R2 = 0.91 (IQR: 0.09), ER = 8.70 (IQR: 4.62). Overall performance increased to R2 = 0.97 (IQR: 0.02) and ER = 4.60 (IQR: 3.68) when parameters from the different bands were combined, further improving when was applied to segments with different inspiration-expiration patterns. In particular, the nose-nose ER = 1.39 (IQR: 0.73), nose-mouth ER = 2.11 (IQR: 1.23) and mouth-mouth ER = 2.29 (IQR: 1.44) patterns showed the best results compared to those obtained for basal, shallow and deep breathing.Clinical relevance- Respiratory pattern variability can be described using multivariable regression model for tidal volume.

Respiratory Pattern Analysis for Different Breathing Types and Recording Sensors in Healthy Subjects.

Accurate monitoring of respiratory activity can lead to early identification and treatment of possible respiratory failure. However, spontaneous breathing can vary considerably. To quantify this variability, this study aimed at comparing the breathing pattern characteristics obtained from several recording sensors during different breathing types. Respiratory activity was recorded with a pneumotachograph and two inductive plethysmographic bands, thoracic and abdominal, in 23 healthy volunteers (age 21.5±1.2 years, 13 females). The subjects were asked to breathe at their natural rate, in successive stages: first freely, then through their nose, nose and mouth, mouth alone, and finally deep and shallow. Both band signals were compared to the pneumotach-derived (gold standard) volume signal. The time series of inspiratory and expiratory duration, total cycle duration and tidal volume were estimated from each of these signals, and also from the sum of the thoracic and abdominal bands. This composite signal showed the highest correlation with the volume signal for almost all subjects, and also had a significantly higher correlation with those obtained from the gold standard volume, compared to either band. In general, breathing parameters increased from basal to nose-mouth breathing, had a minimum in shallow breathing and a maximum in deep breathing. Women exhibited a significantly longer exhalation phase than men during deep breathing, in the combined bands and the gold standard volume. In conclusion, variations in respiratory cycle morphology in different breathing types can be well captured by the simple addition of abdominal and thoracic band signals.Clinical Relevance- Breathing pattern variability can be identified by the combination of abdominal and thoracic bands.