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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 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 (<inline-formula> <tex-math notation="LaTeX">$A_{z}$ </tex-math></inline-formula>) 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 (<inline-formula> <tex-math notation="LaTeX">$AR_{1}$ </tex-math></inline-formula>), downward inspiration (<inline-formula> <tex-math notation="LaTeX">$AR_{2}$ </tex-math></inline-formula>), and the sum of <inline-formula> <tex-math notation="LaTeX">$AR_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$AR_{2}$ </tex-math></inline-formula>, with an <inline-formula> <tex-math notation="LaTeX">$A_{z}$ </tex-math></inline-formula> of 0.892, 0.803, and 0.793, respectively. The proposed method obtained an average accuracy of 94.52&#x0025;, sensitivity of 97.67&#x0025;, and specificity of 90&#x0025; 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.

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