Document Type : Research Article
Authors
1
Department of Biomedical Engineering College of Medical Science and Technologies, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran.
2
Medical Sciences & Technologies Faculty, Science & Research Branch, Islamic Azad University, Tehran, Iran
10.24200/sci.2023.61685.7440
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a common respiratory disease characterized by chronic inflammation of the lung airways and destruction of lung tissue that leads to air restriction. Asthma and Chronic Obstructive Pulmonary Disease are the most common respiratory diseases that cause the death of about 180,000 people worldwide every year, and the death rate of COPD is eight times higher than the death rate of asthma. It is the third leading cause of death worldwide. Time-frequency transformation has been used to diagnose and evaluate the severity of this disease using recorded signals, which are dynamic and non-static. In this research, the S transform is used as a tool to extract features from the lung signal. S transform has a higher frequency resolution than the wavelet transforms at low frequencies, and at high frequencies, it has a lower frequency resolution but a higher time resolution. After feature extraction using S transformation, mathematical statistics were applied to reduce feature dimensions. The results indicate that with K-fold validation for KNN classification, the accuracy, precision, and sensitivity values are 98.39%, 97.45%, and 93.88%, respectively, and for SVM, the results are 95.23%, 92.59%, and 83.33%.
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