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Automatic Quantitative Analysis of Human Respired Carbon Dioxide Waveform for Asthma and Non-Asthma Classification Using Support Vector Machine

Singh, Om Prakash, Palaniappan, Ramaswamy, Malarvilli, M.B. (2018) Automatic Quantitative Analysis of Human Respired Carbon Dioxide Waveform for Asthma and Non-Asthma Classification Using Support Vector Machine. IEEE Access, . ISSN 2169-3536. (doi:10.1109/ACCESS.2018.2871091)

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Abstract

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 progress was made, and no translation into clinical practice has been achieved. Therefore, this study 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-one-out (LOO) cross-validation procedure by employing a support vector machine (SVM). Our results show maximum 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.

Item Type: Article
DOI/Identification number: 10.1109/ACCESS.2018.2871091
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
R Medicine > R Medicine (General) > R858 Medical informatics
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 22 Sep 2018 11:06 UTC
Last Modified: 09 Jul 2019 11:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69211 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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