Key result
A similarity-based fuzzy classification method for ECG and capnogram signals achieved 100% sensitivity, 94.4% specificity, and 97.6% accuracy for atrial fibrillation classification.
Why the study?
Does a similarity-based fuzzy classification method accurately detect atrial fibrillation from ECG signals?
Does a similarity-based fuzzy classification method accurately detect atrial fibrillation from ECG signals?
A novel similarity-based fuzzy classification method demonstrates high accuracy and low computational cost for detecting atrial fibrillation from ECG signals.
May support automated AF screening; leaves open prospective clinical validation before practice change.
A method for ECG and capnogram signals classification is proposed based on fuzzy similarity evaluation, where shape exchange algorithm and fuzzy inference are combined. It aims to be applied to quasi-periodic biomedical signals and has low computational cost. On the experiments for atrial fibrillation (AF) classification using two databases: MIT-BIH AF and MITBIH Normal Sinus Rhythm, values of 100%, 94.4%, and 97.6% for sensitivity, specificity, and accuracy respectively, and execution time of 0.6 s are obtained. The proposal is capable of been extended to classify different diseases, from ECG and capnogram signals, such as: Brugada syndrome, AV block, hypoventilation, and asthma among others to be implemented in low computational resources devices.
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Betancourt et al. (2013) studied Atrial fibrillation. Similarity-based fuzzy classification method was evaluated on Atrial fibrillation classification accuracy. A similarity-based fuzzy classification method for ECG and capnogram signals achieved 100% sensitivity, 94.4% specificity, and 97.6% accuracy for atrial fibrillation classification.
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