Reference-Free Adjustment of Respiratory Inductance Plethysmography for Measurements during Physical Exercise

Respiratory inductance plethysmography (RIP) provides an unobtrusive method for measuring breathing characteristics. Accurately adjusted RIP provides reliable measurements of ventilation during rest and exercise if data are acquired via two elastic measuring bands surrounding the rib cage (RC) and abdomen (AB). Disadvantageously, the most accurate reported adjusted model for RIP in literature-least squares regression-requires simultaneous RIP and flowmeter (FM) data acquisition. An adjustment method without simultaneous measurement (reference-free) of RIP and FM would foster usability enormously.

In this paper, we develop generalizable, functional, and reference-free algorithms for RIP adjustment incorporating anthropometric data. Further, performance of only one-degree of freedom (RC or AB) instead of two (RC and AB) is investigated. We evaluate the algorithms with data from 193 healthy subjects who performed an incremental running test using three different datasets: training, reliability, and validation dataset. The regression equation is improved with machine learning techniques such as sequential forward feature selection and 10-fold cross validation.

Using the validation dataset, the best reference-free adjustment model is the combination of both bands with 84.69% breaths within  20% limits of equivalence compared to 43.63% breaths using the best comparable algorithm from literature. Using only one band, we obtain better results using the RC band alone.

Reference-free adjustment for RIP reveals tidal volume differences of up to 0.25 l when comparing to the best possible adjustment currently present which needs the simultaneous measurement of RIP and FM.

This demonstrates that RIP has the potential for usage in wide applications in ambulatory settings.