An Effective Machine Learning Approach for Classifying Artefact-Free and Distorted Capnogram Segments Using Simple Time-Domain Features

Capnogram signal analysis has received considerable attention owing to its important applications in assessing cardiopulmonary functions. However, the automatic elimination of deformed parts of a capnogram waveform remains an open research problem. Herein, we introduce an automatic classification approach for discriminating artefact-free (regular) and distorted (irregular) segments of capnogram signals. The proposed features include Hjorth parameters and mean absolute deviation (MAD). The main advantage of these features is their simplicity, such that they can be employed in a computationally efficient machine learning algorithm. MATLAB simulation is conducted on 100 regular and 100 irregular segments of capnogram to extract the proposed and existing features, which are ranked based on the Pearson correlation coefficient,
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-value and area under receiver operating characteristic (ROC) curve. The naive Bayes, decision tree, random forest and support vector machine (SVM) classifiers are fed by the relatively highly ranked features, and the classification performance is assessed via ten-fold cross-validation. Besides the linear kernel SVM, the radial basis function (RBF) and polynomial kernel functions with different orders are also included in the current experiment. Results revealed the effectiveness of the Hjorth activity and MAD attributes when used with the fourth-order polynomial kernel-SVM classifier. The achieved accuracy, precision and specificity are 89%, 92.1%, and 91% outperforming the existing method by 2.5%, 5.6% and 7%, respectively. The simplicity of the proposed time-domain features is confirmed by the average total computational time of features extraction and classification phases which is only 13 ms instead of 19 ms in the case of incorporating both time- and frequency-domain features, indicating a reduction of 31.6%. It is envisaged that the proposed approach can be valuable if implemented with capnography devices for real-time and fully automated capnogram-based respiratory assessment. Even so, further research is recommended to enhance the classification performance through exploring more features and/or classifiers.