Skip to main content

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases.

Albiges, T., Sabeur, Z. and Arbab-Zavar, B., 2023. Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases. Sensors, 23 (3), 1439.

Full text available as:

sensors-23-01439.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.3390/s23031439


Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) “Healthy” or “COPD” and (2) “Healthy”, “COPD”, or “Pneumonia” classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future.

Item Type:Article
Additional Information:This article belongs to the Special Issue Advances in Artificial Intelligence for Biomedical Signal and Image Analysis
Uncontrolled Keywords:artificial intelligence; machine learning; COPD; compressed sensing; signals reconstruction; dictionary learning
Group:Faculty of Science & Technology
ID Code:38223
Deposited By: Symplectic RT2
Deposited On:17 Feb 2023 17:38
Last Modified:22 May 2024 15:45


Downloads per month over past year

More statistics for this item...
Repository Staff Only -