Classification of severity of trachea stenosis from EEG signals using ordinal decision-tree based algorithms and ensemble-based ordinal and non-ordinal algorithms

Gonen Singer, Anat Ratnovsky, Sara Naftali

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Machine learning is integrated nowadays in many data-driven applications that attempt to model the behavior of a system. Thus, the implementation of machine-learning algorithms for medical applications is growing, enabling doctors to make decisions based on the output of the model of the system's behavior. The upper airway is involved in a variety of disorders that lead to non-specific symptoms; thus, upper-airway obstruction is frequently unrecognized or misdiagnosed. Bronchoscopy, which is a minimally invasive procedure, and lung function (spirometry) tests, which are relatively demanding for the patient, are currently the most common methods for diagnosing respiratory diseases. In this study, a novel, non-invasive procedure is proposed in which tracheal obstruction is identified based on brain signals. Specifically, the spectral information in electroencephalogram (EEG) signals is used as an input to an ensemble learner approach based on ordinal and non-ordinal classification algorithms, where the classification problem involves identifying the degree of airway obstruction. An experiment was conducted in which four healthy subjects breathed through three-dimensional (3D) geometric models of the trachea that mimicked different obstruction rates. Multi-subject classification was carried out in which the classification model of each subject was produced by training the model on the other subjects' datasets. The main findings were as follows. Firstly, the in-house ordinal classification algorithms, which included a C4.5 and a random-forest algorithm, both based on a weighted information-gain ratio measure, yielded better classification results than their non-ordinal counterparts and other conventional classifiers. Additionally, the study showed that when integrating the two types of algorithms (ordinal and non-ordinal) into an ensemble approach, the performance was improved relative to each individual classifier. Finally, the classification accuracy is such that the proposed method of using EEG signals for the identification of the degree of tracheal obstruction by means of an ensemble approach shows promise as a supplemental clinical test.

Original languageEnglish
Article number114707
JournalExpert Systems with Applications
Volume173
DOIs
StatePublished - 1 Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Airway obstruction
  • Electroencephalogram
  • Ensemble learning
  • Machine learning
  • Ordinal classification

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