Respiratory volume monitoring: A machine-learning approach to the non-invasive prediction of tidal volume and minute ventilation

Abstract

Continuous monitoring of ventilatory parameters such as tidal volume (TV) and minute ventilation (MV) has shown to be effective in the prevention of respiratory compromise events in hospitalized patients. However, the non-invasive estimation of respiratory volume in non-intubated patients remains an outstanding challenge. In this work, we present a novel approach to respiratory volume monitoring (RVM) that continuously predicts TV and MV in normal subjects. Respiratory flow in 19 volunteers under spontaneous breathing was recorded using respiratory inductance plethysmography and a temperature-based wearable sensor. Temperature signals were processed to identify features such as temperature amplitude and mean value, among others. The feature datasets were then used to train and validate three machine-learning (ML) algorithms for the prediction of respiratory volume based on temperature-related features. A model based on Random-Forest regression resulted in the lowest root mean-square error and was subsequently chosen to predict ventilatory parameters on subject test data not used in the construction of the model. Our predictions achieve a bias (mean error) in TV and MV of 16.04 mL and 0.19 L/min, respectively, which compare well with performance metrics reported in commercially-available RVM systems based on electrical impedance. Our results show that the combination of novel respiratory temperature sensors and machine-learning algorithms can deliver accurate and continuous estimates of TV and MV in healthy subjects