Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation

Daniel E Hurtado,Roberto Mansilla,Javier A P Chavez,Angel Abusleme,Roberto Lopez

doi:10.1109/access.2020.3045603

Daniel E Hurtado, Roberto Mansilla + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3045603

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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.

Highlights

T HE development of respiratory monitoring systems has received continuous attention as respiratory parameters such as rate of breathing are routinely assessed in the physical examination of patients [1], [2]
The correlation matrix for the input features is shown in Figure 3, where we observe that the absolute correlation between different features does not exceed 0.8
We kept M Ro and M Rn as input features in the model as they are important physiological quantities that inform us about the rate of the breathing process, which is not contained in the ACo or ACn values

Summary

Introduction

T HE development of respiratory monitoring systems has received continuous attention as respiratory parameters such as rate of breathing are routinely assessed in the physical examination of patients [1], [2]. Abnormal values in respiratory parameters are currently recognized as early signs of patient deterioration, and have long been associated to respiratory failure in non-intubated patients [3], in-hospital cardiac arrest [4], and post-anesthesia respiratory depression [5], among others. Respiratory monitoring is a common practice in intubated patients undergoing mechanical ventilation, but remains an open challenge in non-intubated patients that breathe spontaneously [6]. Current gold-standard methods for nonintubated patients available in the clinical setting are based on capnometry and impedance pneumography systems, which provide continuous estimates of the respiratory rate (RR).

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