Key result
Segmented wavelet decomposition for capnogram feature extraction achieved a 97.39% best classification accuracy for asthma in real time, averaging 8 seconds per determination.
A segmented wavelet decomposition method for capnogram feature extraction provides high accuracy and low computational cost for real-time asthma classification.
May support real-time capnogram asthma classification; hypothesis-generating and requires larger prospective validation before clinical use.
A feature extraction method from capnograms used for classifying asthma is proposed based on wavelet decomposition. Its computational cost is low and its performance is adequate for classifying asthma in real time. Experiments performed using 23 capnograms from an asthma camp in Cuba showed 97.39% best classification accuracy. The time required for a physiological multiparameter monitor to determine the suitable features of capnograms averaged 8 seconds. The proposal is to be used as part of a decision support system for asthma classification being developed by TITECH and TMDU research groups.
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Betancourt et al. (2014) studied Asthma (n=23). Segmented Wavelet Decomposition for Capnogram Feature Extraction was evaluated on Classification accuracy. Segmented wavelet decomposition for capnogram feature extraction achieved a 97.39% best classification accuracy for asthma in real time, averaging 8 seconds per determination.
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