Classification of Electrical Impedance Tomography Data Using Machine Learning

TL;DR

This paper explores machine learning models applied to Electrical Impedance Tomography data to differentiate healthy from non-healthy lung conditions, aiming to enhance clinical diagnosis and monitoring of pulmonary diseases.

Contribution

It introduces a new feature, Impedance Curve Correlation, and demonstrates initial classification accuracy using machine learning on EIT data from 16 subjects.

Findings

66% accuracy in challenging scenarios

Potential for improved diagnostic tools

Introduction of a novel feature for EIT analysis

Abstract

Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a non-invasive imaging method that allows to analyze and quantify the distribution of ventilation in the lungs. In this article, we present a new approach to promote the use of EIT data and the implementation of new clinical applications for differential diagnosis, with the development of several machine learning models to discriminate between EIT data from healthy and non-healthy subjects. EIT data from 16 subjects were acquired: 5 healthy and 11 non-healthy subjects (with multiple pulmonary conditions). Preliminary results have shown accuracy percentages of 66\% in challenging evaluation scenarios. The results suggest that the pairing of EIT feature engineering methods with machine learning methods could be further explored and applied in the diagnostic and monitoring of patients suffering from lung diseases. Also, we introduce the use of a new feature in the context of EIT data analysis (Impedance Curve Correlation).