Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine
Currently, carbon dioxide (CO2) waveforms measured by capnography are used to estimate respiratory rate and end-tidal CO2 (EtCO2) in the clinic. However, the shape of the CO2 signal carries significant diagnostic information about the asthmatic condition. Previous studies have shown a strong correla...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers Inc.
2018
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| author | Singh, Om Prakash Palaniappan, Ramaswamy Malarvili, Mb. |
| author_facet | Singh, Om Prakash Palaniappan, Ramaswamy Malarvili, Mb. |
| author_sort | Singh, Om Prakash |
| collection | ePrints |
| description | Currently, carbon dioxide (CO2) waveforms measured by capnography are used to estimate respiratory rate and end-tidal CO2 (EtCO2) in the clinic. However, the shape of the CO2 signal carries significant diagnostic information about the asthmatic condition. Previous studies have shown a strong correlation between various features that quantitatively characterize the shape of CO2 signal and are used to discriminate asthma from non-asthma using pulmonary function tests, but no reliable progresswas made, and no translation into clinical practice has been achieved. Therefore, this paper reports a relatively simple signal processing algorithm for automatic differentiation of asthma and non-asthma. CO2 signals were recorded from 30 non-asthmatic and 43 asthmatic patients. Each breath cycle was decomposed into subcycles, and features were computationally extracted. Thereafter, feature selection was performed using the area (Az) under the receiver operating characteristics curve analysis. A classification was performed via a leave-oneout cross-validation procedure by employing a support vector machine. Our results showmaximum screening capabilities for upward expiration (AR1), downward inspiration (AR2), and the sum of AR1 and AR2, with an Az of 0.892, 0.803, and 0.793, respectively. The proposed method obtained an average accuracy of 94.52%, sensitivity of 97.67%, and specificity of 90% for discrimination of asthma and non-asthma. The proposed method allows for automatic classification of asthma and non-asthma condition by analyzing the shape of the CO2 waveform. The developed method may possibly be incorporated in real-time for assessment and management of the asthmatic conditions. |
| first_indexed | 2024-03-05T20:33:09Z |
| format | Article |
| id | utm.eprints-84545 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:33:09Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | utm.eprints-845452020-01-11T07:32:35Z http://eprints.utm.my/84545/ Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine Singh, Om Prakash Palaniappan, Ramaswamy Malarvili, Mb. Q Science (General) Currently, carbon dioxide (CO2) waveforms measured by capnography are used to estimate respiratory rate and end-tidal CO2 (EtCO2) in the clinic. However, the shape of the CO2 signal carries significant diagnostic information about the asthmatic condition. Previous studies have shown a strong correlation between various features that quantitatively characterize the shape of CO2 signal and are used to discriminate asthma from non-asthma using pulmonary function tests, but no reliable progresswas made, and no translation into clinical practice has been achieved. Therefore, this paper reports a relatively simple signal processing algorithm for automatic differentiation of asthma and non-asthma. CO2 signals were recorded from 30 non-asthmatic and 43 asthmatic patients. Each breath cycle was decomposed into subcycles, and features were computationally extracted. Thereafter, feature selection was performed using the area (Az) under the receiver operating characteristics curve analysis. A classification was performed via a leave-oneout cross-validation procedure by employing a support vector machine. Our results showmaximum screening capabilities for upward expiration (AR1), downward inspiration (AR2), and the sum of AR1 and AR2, with an Az of 0.892, 0.803, and 0.793, respectively. The proposed method obtained an average accuracy of 94.52%, sensitivity of 97.67%, and specificity of 90% for discrimination of asthma and non-asthma. The proposed method allows for automatic classification of asthma and non-asthma condition by analyzing the shape of the CO2 waveform. The developed method may possibly be incorporated in real-time for assessment and management of the asthmatic conditions. Institute of Electrical and Electronics Engineers Inc. 2018 Article PeerReviewed Singh, Om Prakash and Palaniappan, Ramaswamy and Malarvili, Mb. (2018) Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine. IEEE Access, 6 (846897). pp. 55245-55256. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2018.2871091 DOI:10.1109/ACCESS.2018.2871091 |
| spellingShingle | Q Science (General) Singh, Om Prakash Palaniappan, Ramaswamy Malarvili, Mb. Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine |
| title | Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine |
| title_full | Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine |
| title_fullStr | Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine |
| title_full_unstemmed | Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine |
| title_short | Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine |
| title_sort | automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non asthma classification using support vector machine |
| topic | Q Science (General) |
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