Work of Breathing Estimation during Spontaneous Breathing Test using Machine Learning Techniques

Abstract

Prolonged support or premature weaning of mechanical ventilation leads to several complications of cardiopulmonary physiology. Recently, work of breathing is proposed as an alternative that provides objective information about the weaning process. However, the availability and ease of use of techniques for its estimation in a clinical context are limited. Thus, the application of computerized methods for work of breathing estimation becomes necessary to assist professionals. In this article, we compare the performance of different machine learning techniques in the work of breathing estimation tasks. The problem is divided into two classes: high and low work of breathing, based on information extracted from the pressure, volume, and flow signals recorded by the mechanical ventilator. The classification algorithms used were: support vector machines, neural networks, k nearest neighbors, which were trained and tested on ventilatory signals of subjects with high and low work of breathing. The results show that the classification system can recognize the work of breathing levels with an accuracy of up to 80%.