A performance comparison of wheeze feature extraction methods for asthma severity levels classification
Asthma is a chronic disease that requires monitoring and treatment throughout the patient's lifetime. The common adventitious sounds related to asthma are wheezes. A study that has classified the severity of asthma using wheezes are still lacking in the field, therefore, the purpose of this wor...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/25686/ http://umpir.ump.edu.my/id/eprint/25686/1/A%20performance%20comparison%20of%20wheeze%20feature%20extraction%20methods%20.pdf |
| _version_ | 1848822343081656320 |
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| author | Syamimi Mardiah, Shaharum Sundaraj, Kenneth Shazmin, Aniza Palaniappan, Rajkumar Helmy, Khaled |
| author_facet | Syamimi Mardiah, Shaharum Sundaraj, Kenneth Shazmin, Aniza Palaniappan, Rajkumar Helmy, Khaled |
| author_sort | Syamimi Mardiah, Shaharum |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Asthma is a chronic disease that requires monitoring and treatment throughout the patient's lifetime. The common adventitious sounds related to asthma are wheezes. A study that has classified the severity of asthma using wheezes are still lacking in the field, therefore, the purpose of this work is to compare feature extraction methods for the classification of asthma severity level. Three types of features opted are mel frequency cepstral coefficients (MFCC); short time energy (STE); auto-regressive model and k-nearest neighbor (KNN) classifier is used in representing the performance of the feature used. Based on the overall performance between the features, MFCC features and KNN classifier shows the best and the highest performance with 95.92%, 96.33% and 98.42% average accuracy, sensitivity and specificity value obtained compared to STE that only obtained the highest average accuracy, sensitivity and specificity value of 84.94%, 87.33% and 95% respectively while AR features only obtained the highest average accuracy, sensitivity and specificity value of 49.43%, 52.17%, and 82.79% respectively. |
| first_indexed | 2025-11-15T02:39:44Z |
| format | Conference or Workshop Item |
| id | ump-25686 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:39:44Z |
| publishDate | 2019 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-256862019-12-17T02:43:01Z http://umpir.ump.edu.my/id/eprint/25686/ A performance comparison of wheeze feature extraction methods for asthma severity levels classification Syamimi Mardiah, Shaharum Sundaraj, Kenneth Shazmin, Aniza Palaniappan, Rajkumar Helmy, Khaled TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Asthma is a chronic disease that requires monitoring and treatment throughout the patient's lifetime. The common adventitious sounds related to asthma are wheezes. A study that has classified the severity of asthma using wheezes are still lacking in the field, therefore, the purpose of this work is to compare feature extraction methods for the classification of asthma severity level. Three types of features opted are mel frequency cepstral coefficients (MFCC); short time energy (STE); auto-regressive model and k-nearest neighbor (KNN) classifier is used in representing the performance of the feature used. Based on the overall performance between the features, MFCC features and KNN classifier shows the best and the highest performance with 95.92%, 96.33% and 98.42% average accuracy, sensitivity and specificity value obtained compared to STE that only obtained the highest average accuracy, sensitivity and specificity value of 84.94%, 87.33% and 95% respectively while AR features only obtained the highest average accuracy, sensitivity and specificity value of 49.43%, 52.17%, and 82.79% respectively. IEEE 2019-03-01 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25686/1/A%20performance%20comparison%20of%20wheeze%20feature%20extraction%20methods%20.pdf Syamimi Mardiah, Shaharum and Sundaraj, Kenneth and Shazmin, Aniza and Palaniappan, Rajkumar and Helmy, Khaled (2019) A performance comparison of wheeze feature extraction methods for asthma severity levels classification. In: 9th IEEE Control And System Graduate Research Colloquium (ICSGRC 2018) , 3-4 August 2018 , Shah Alam, Selangor. pp. 145-150. (8657630). ISBN 978-1-5386-6321-9 (Published) https://doi.org/10.1109/ICSGRC.2018.8657630 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Syamimi Mardiah, Shaharum Sundaraj, Kenneth Shazmin, Aniza Palaniappan, Rajkumar Helmy, Khaled A performance comparison of wheeze feature extraction methods for asthma severity levels classification |
| title | A performance comparison of wheeze feature extraction methods for asthma severity levels classification |
| title_full | A performance comparison of wheeze feature extraction methods for asthma severity levels classification |
| title_fullStr | A performance comparison of wheeze feature extraction methods for asthma severity levels classification |
| title_full_unstemmed | A performance comparison of wheeze feature extraction methods for asthma severity levels classification |
| title_short | A performance comparison of wheeze feature extraction methods for asthma severity levels classification |
| title_sort | performance comparison of wheeze feature extraction methods for asthma severity levels classification |
| topic | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/25686/ http://umpir.ump.edu.my/id/eprint/25686/ http://umpir.ump.edu.my/id/eprint/25686/1/A%20performance%20comparison%20of%20wheeze%20feature%20extraction%20methods%20.pdf |