Asthma severity identification from pulmonary acoustic signal for computerized decision support system
Abstract Objective: Breath sound has information about underlying pathology and condition of subjects. The purpose of this study is to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis has been extended to observ...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Pakistan Medical Association
2020-10-01
|
| Series: | Journal of the Pakistan Medical Association |
| Online Access: | https://www.ojs.jpma.org.pk/index.php/public_html/article/view/1645 |
| _version_ | 1797820595974438912 |
|---|---|
| author | Fizza Ghulam Nabi Kenneth Sundaraj Chee Kiang Lam |
| author_facet | Fizza Ghulam Nabi Kenneth Sundaraj Chee Kiang Lam |
| author_sort | Fizza Ghulam Nabi |
| collection | DOAJ |
| description |
Abstract
Objective: Breath sound has information about underlying pathology and condition of subjects. The purpose of this study is to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis has been extended to observe behavior of wheeze sounds in different datasets.
Method: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) has been calculated from normalized power spectrum. Subsequently, multivariate analysis has been performed for analysis.
Result: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level ? = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ????2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples ? = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ????2 = 0.386-0.568.
Conclusion: The results demonstrate that severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics.
Keywords: Asthma, Breath Sounds, Wheeze Detection, Airway Obstruction, Severity Level
Continuous...
|
| first_indexed | 2024-03-13T09:40:38Z |
| format | Article |
| id | doaj.art-90b3ca0fe6974c97b19373af8e2d459f |
| institution | Directory Open Access Journal |
| issn | 0030-9982 |
| language | English |
| last_indexed | 2024-03-13T09:40:38Z |
| publishDate | 2020-10-01 |
| publisher | Pakistan Medical Association |
| record_format | Article |
| series | Journal of the Pakistan Medical Association |
| spelling | doaj.art-90b3ca0fe6974c97b19373af8e2d459f2023-05-25T04:25:27ZengPakistan Medical AssociationJournal of the Pakistan Medical Association0030-99822020-10-0110.47391/JPMA.156Asthma severity identification from pulmonary acoustic signal for computerized decision support systemFizza Ghulam Nabi 0Kenneth Sundaraj1Chee Kiang Lam2Institute of Quality and Technology Management, University of the Punjab, Lahore, PakistanCentre for Telecommunication Research Innovation CeTRI, Fakulti Kejuruteraan Elektronik Dan Kejuruteraan Komputer FKEKK, Universiti Teknikal Malaysia Melaka UTeM;School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Malaysia Abstract Objective: Breath sound has information about underlying pathology and condition of subjects. The purpose of this study is to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis has been extended to observe behavior of wheeze sounds in different datasets. Method: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) has been calculated from normalized power spectrum. Subsequently, multivariate analysis has been performed for analysis. Result: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level ? = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ????2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples ? = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ????2 = 0.386-0.568. Conclusion: The results demonstrate that severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics. Keywords: Asthma, Breath Sounds, Wheeze Detection, Airway Obstruction, Severity Level Continuous... https://www.ojs.jpma.org.pk/index.php/public_html/article/view/1645 |
| spellingShingle | Fizza Ghulam Nabi Kenneth Sundaraj Chee Kiang Lam Asthma severity identification from pulmonary acoustic signal for computerized decision support system Journal of the Pakistan Medical Association |
| title | Asthma severity identification from pulmonary acoustic signal for computerized decision support system |
| title_full | Asthma severity identification from pulmonary acoustic signal for computerized decision support system |
| title_fullStr | Asthma severity identification from pulmonary acoustic signal for computerized decision support system |
| title_full_unstemmed | Asthma severity identification from pulmonary acoustic signal for computerized decision support system |
| title_short | Asthma severity identification from pulmonary acoustic signal for computerized decision support system |
| title_sort | asthma severity identification from pulmonary acoustic signal for computerized decision support system |
| url | https://www.ojs.jpma.org.pk/index.php/public_html/article/view/1645 |
| work_keys_str_mv | AT fizzaghulamnabi asthmaseverityidentificationfrompulmonaryacousticsignalforcomputerizeddecisionsupportsystem AT kennethsundaraj asthmaseverityidentificationfrompulmonaryacousticsignalforcomputerizeddecisionsupportsystem AT cheekianglam asthmaseverityidentificationfrompulmonaryacousticsignalforcomputerizeddecisionsupportsystem |