JACIII Vol.17 No.2 pp. 302-310
doi: 10.20965/jaciii.2013.p0302


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

November 26, 2012
February 17, 2013
March 20, 2013
classification, similarity, fuzzy inference, ECG, capnogram

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.

Cite this article as:
J. Betancourt, C. Fatichah, <. Tangel, F. Yan, <. Sanchez, F. Dong, and <. 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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Last updated on Jan. 21, 2019