Feature Engineering and Supervised Learning Classifiers for Respiratory Artefact Removal in Lung Function Tests

Abstract

A critical task in forced oscillation technique (FOT), a promising lung function test, is to remove respiratory artefacts. Manual removal by specialists is widely used but time- consuming and subjective. Most existing automated techniques have involved simple thresholding methods in an unsupervised manner. Breath cycles can be classified by a binary classification model (classes: artefactual and accepted). While attempting to use off-the-shelf sorting algorithms (e.g., one-class support vector machine, knearest neighbours, and adaptive boosting ensemble), we noticed their poor detection performance. This may result from the dependence of samples as found in physiological studies of the lung function that challenges the learning process. Specifically, statistics of breaths that we recorded may change from one to another patient and even within the same recording of a patient. We introduce an additional feature engineering step that is an intermediate module to decorrelate samples, called feature learning (using Wilcoxon signed rank tests). To that end, we collected FOT recordings from various groups of patients (paediatric and adult including healthy and asthmatics). Artefacts in this work were recorded naturally and processed in a complete-breath approach. Performance metrics include evaluations on preservation of "accepted" breaths in the filtered output (including F1- score, throughput, and approval rate). Our experiment found that our feature engineering steps significantly improve the artefact removal performance of all implemented classifiers especially with feature inputs selected by mutual information criterion.