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Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis.

Do-Kyeong Lee¹, Jae-Sung Choi², Seong-Jun Choi³

¹Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

Diagnostics (Basel, Switzerland)

|February 13, 2025

Summary

This study developed a non-contact method to classify individuals with and without chronic obstructive pulmonary disease (COPD) using thermal imaging and respiratory data analysis. The system achieved 82.5% accuracy, showing promise for remote respiratory disease diagnosis.

Keywords:

COPD image preprocessing non-contact diagnosis thermal camera

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Area of Science:

Medical Imaging
Respiratory Medicine
Data Science

Background:

Chronic Obstructive Pulmonary Disease (COPD) poses a significant global health challenge.
Accurate and early diagnosis of COPD is crucial for effective patient management.
Current diagnostic methods can be invasive or require specialized equipment.

Purpose of the Study:

To propose a novel classification system for predicting COPD patients and non-patients.
To leverage thermal imaging and respiratory pattern analysis for non-contact disease detection.
To develop a data-driven approach for objective COPD assessment.

Main Methods:

Respiratory volume was measured using thermal imaging.
Key respiratory pattern features (volume, expiration/inspiration intervals, rate) were extracted and stored as text data.
Features were normalized (Z-score) and combined via weighted summation to generate a classification score.
Receiver Operating Characteristic (ROC) curve analysis determined the optimal threshold for patient classification.

Main Results:

The classification system achieved an overall accuracy of 82.5%.
Precision, recall, and F1-score metrics confirmed the model's high classification performance.
The study successfully demonstrated the feasibility of using thermal imaging for COPD detection.

Conclusions:

The proposed non-contact classification system shows significant potential for diagnosing respiratory diseases like COPD.
This approach could facilitate remote medical examinations and improve accessibility to diagnostic tools.
Further research may lead to widespread clinical application in non-invasive respiratory health monitoring.