Similarity-Based Fuzzy Classification of ECG and Capnogram Signals
Janet Pomares Betancourt*,**, Chastine Fatichah*,
Martin Leonard Tangel*, Fei Yan*,
Jesus Adrian Garcia Sanchez*, Fang-Yan Dong*,
and Kaoru Hirota*
*Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**Central Institute of Digital Research, 202, No.1704, Siboney, Playa, Havana, Cuba
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.
Martin Leonard Tangel, Fei Yan,
Jesus Adrian Garcia Sanchez, Fang-Yan Dong, and
and Kaoru Hirota, “Similarity-Based Fuzzy Classification of ECG and Capnogram Signals,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.2, pp. 302-310, 2013.
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