Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach
This paper presents a machine learning-based approach for the automatic classification of regular and irregular capnogram segments. METHODS: Herein, we proposed four time- and two frequency-domain features experimented with the support vector machine classifier through ten-fold cross-validation. MAT...
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my.utm.941792022-02-28T13:17:41Z http://eprints.utm.my/id/eprint/94179/ Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach El-Badawy, I. M. Singh, O. P. Omar, Z. TK Electrical engineering. Electronics Nuclear engineering This paper presents a machine learning-based approach for the automatic classification of regular and irregular capnogram segments. METHODS: Herein, we proposed four time- and two frequency-domain features experimented with the support vector machine classifier through ten-fold cross-validation. MATLAB simulation was conducted on 100 regular and 100 irregular 15 s capnogram segments. Analysis of variance was performed to investigate the significance of the proposed features. Pearson's correlation was utilized to select the relatively most substantial ones, namely variance and the area under normalized magnitude spectrum. Classification performance, using these features, was evaluated against two feature sets in which either time- or frequency-domain features only were employed. RESULTS: Results showed a classification accuracy of 86.5%, which outperformed the other cases by an average of 5.5%. The achieved specificity, sensitivity, and precision were 84%, 89% and 86.51%, respectively. The average execution time for feature extraction and classification per segment is only 36 ms. CONCLUSION: The proposed approach can be integrated with capnography devices for real-time capnogram-based respiratory assessment. However, further research is recommended to enhance the classification performance. IOS Press BV 2021 Article PeerReviewed El-Badawy, I. M. and Singh, O. P. and Omar, Z. (2021) Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach. Technology and Health Care, 29 (1). pp. 59-72. ISSN 0928-7329 http://www.dx.doi.org/10.3233/THC-202198 DOI: 10.3233/THC-202198 |
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TK Electrical engineering. Electronics Nuclear engineering El-Badawy, I. M. Singh, O. P. Omar, Z. Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach |
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This paper presents a machine learning-based approach for the automatic classification of regular and irregular capnogram segments. METHODS: Herein, we proposed four time- and two frequency-domain features experimented with the support vector machine classifier through ten-fold cross-validation. MATLAB simulation was conducted on 100 regular and 100 irregular 15 s capnogram segments. Analysis of variance was performed to investigate the significance of the proposed features. Pearson's correlation was utilized to select the relatively most substantial ones, namely variance and the area under normalized magnitude spectrum. Classification performance, using these features, was evaluated against two feature sets in which either time- or frequency-domain features only were employed. RESULTS: Results showed a classification accuracy of 86.5%, which outperformed the other cases by an average of 5.5%. The achieved specificity, sensitivity, and precision were 84%, 89% and 86.51%, respectively. The average execution time for feature extraction and classification per segment is only 36 ms. CONCLUSION: The proposed approach can be integrated with capnography devices for real-time capnogram-based respiratory assessment. However, further research is recommended to enhance the classification performance. |
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Article |
author |
El-Badawy, I. M. Singh, O. P. Omar, Z. |
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El-Badawy, I. M. Singh, O. P. Omar, Z. |
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El-Badawy, I. M. |
title |
Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach |
title_short |
Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach |
title_full |
Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach |
title_fullStr |
Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach |
title_full_unstemmed |
Automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach |
title_sort |
automatic classification of regular and irregular capnogram segments using time- and frequency-domain features: a machine learning-based approach |
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IOS Press BV |
publishDate |
2021 |
url |
http://eprints.utm.my/id/eprint/94179/ http://www.dx.doi.org/10.3233/THC-202198 |
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1726791493095522304 |