Scientia Iranica

Scientia Iranica

Diagnosing and evaluating the severity of chronic obstructive pulmonary disease based on the time-frequency features of the S-transform applied to the lung sound signal

Document Type : Research Article

Authors
Faculty of Medical Sciences and Technologies, Science and 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 airflow limitation. Asthma and COPD are the two most common respiratory diseases that together cause approximately 180,000 deaths worldwide every year. Moreover, the death rate of COPD is eight times higher than the death rate of Asthma. COPD is the third leading cause of death worldwide. Time-frequency transform 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 Stockwell transform (S-transform) is used as a tool to extract features from the lung signal. S-transform has a higher frequency resolution than wavelet transforms at low frequencies, and at high frequencies, it has a lower frequency resolution but a higher time resolution. After feature extraction using the S-transform, mathematical statistics were applied to reduce feature dimensions. The results indicate that with K-fold validation for K-Nearest Neighbors (KNN) classification, the accuracy, precision, and sensitivity values are 98.39%, 97.45%, and 93.88%, respectively. For Support Vector Machine (SVM), the results are 95.23%, 92.59%, and 83.33%, respectively.
Keywords
Subjects

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Volume 32, Issue 19
Transactions on Computer Science & Engineering and Electrical Engineering
November and December 2025 Article ID:7440

  • Receive Date 31 December 2022
  • Revise Date 03 July 2023
  • Accept Date 19 November 2023