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, Kaoru Hirota

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)302-310
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number2
Publication statusPublished - Mar 2013
Externally publishedYes


  • Capnogram
  • Classification
  • ECG
  • Fuzzy inference
  • Similarity


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