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

Currently, carbon dioxide (CO2waveforms measured by capnography are used to estimate respiratory rate and end-tidal CO2(EtCO2) in the clinic. However, the shape of the CO2signal carries significant diagnostic information about the asthmatic condition. Previous studies have shown a strong correlation between various features that quantitatively characterize the shape of CO2signal 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 paper reports a relatively simple signal processing algorithm for automatic differentiation of asthma and non-asthma. CO2signals 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 cross-validation procedure by employing a support vector machine. Our results show maximum screening capabilities for upward expiration (AR₁), downward inspiration (AR₂), and the sum ofAR₁andAR₂, with anAzof 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 CO2waveform. The developed method may possibly be incorporated in real-time for assessment and management of the asthmatic conditions.