Feature engineering and supervised learning classifiers for respiratory artefact removal in lung function tests

Thuy T. Pham, Diep N. Nguyen, Eryk Dutkiewicz, Alistair L. McEwan, Cindy Thamrin, Paul D. Robinson, Philip H. W. Leong

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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.

Original languageEnglish
Title of host publication2016 IEEE Global Communications Conference (GLOBECOM)
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781509013289 , 9781509013272
ISBN (Print)9781509013296
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
Duration: 4 Dec 20168 Dec 2016

Other

Other59th IEEE Global Communications Conference, GLOBECOM 2016
CountryUnited States
CityWashington
Period4/12/168/12/16

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