Cooperative classification of clean and deformed capnogram segments using a voting approach: A trade-off between specificity and sensitivity

Automatic discrimination of clean and deformed segments of capnogram signals is an essential requisite in capnogram-based respiratory assessment. However, improving the performance of this classification task remains challenging, particularly in terms of specificity and sensitivity. The goal of this paper is to address this issue by proposing a cooperative classification approach rather than relying solely on a single classifier. The presented method's main advantage is the vote participation of four distinct classifiers that affects the reliability of the final classification decision. MATLAB simulation was run on a dataset consisting of 200 15-seconds capnogram segments, 100 of which are clean and 100 are deformed. The results revealed a trade-off between the achieved specificity and sensitivity by adjusting the strictness of voting. Being highly strict in the sense of classifying a capnogram segment as clean if and only if all voting classifiers agreed on deciding so, provided specificity and sensitivity of 94% and 81%, respectively. On the contrary, lowering the strictness of voting by considering only one positive vote is sufficient to eventually classify the query capnogram segment as non-deformed gave specificity and sensitivity of 74% and 94%, respectively.