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

Currently, carbon dioxide (CO
₂
waveforms measured by capnography are used to estimate respiratory rate and end-tidal CO
₂
(EtCO
₂
) in the clinic. However, the shape of the CO
₂
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 CO
₂
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. CO
₂
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 (
$A_{z}$
) 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 (
$AR_{1}$
), downward inspiration (
$AR_{2}$
), and the sum of
$AR_{1}$
and
$AR_{2}$
, with an
$A_{z}$
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 CO
₂
waveform. The developed method may possibly be incorporated in real-time for assessment and management of the asthmatic conditions.