Similarity-Based Fuzzy Classification of ECG and Capnogram Signals

Author:

Betancourt Janet Pomares, ,Fatichah Chastine,Tangel Martin Leonard,Yan Fei,Sanchez Jesus Adrian Garcia,Dong Fang-Yan,Hirota Kaoru,

Abstract

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.

Publisher

Fuji Technology Press Ltd.

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Techniques and Methods for Anomalies Detection in ECG as a Support for Medical Decision in Healthcare: A Review;2023 International Conference on Decision Aid Sciences and Applications (DASA);2023-09-16

2. Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis;Automatika;2016-01

3. A Hybrid Particle Swarm Optimization and Neural Network with Fuzzy Membership Function Technique for Epileptic Seizure Classification;Journal of Advanced Computational Intelligence and Intelligent Informatics;2015-05-20

4. Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal;Journal of Advanced Computational Intelligence and Intelligent Informatics;2015-03-20

5. A Combined AdaBoost and NEWFM Technique for Medical Data Classification;Lecture Notes in Electrical Engineering;2015

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