Similarity-Based Fuzzy Classification of ECG and Capnogram Signals

被引：6

作者：

Pomares Betancourt, Janet ^{[1
,2
]}

Fatichah, Chastine ^{[1
]}

Leonard Tangel, Martin ^{[1
]}

Yan, Fei ^{[1
]}

Sanchez, Jesus Adrian Garcia ^{[1
]}

Dong, Fang-Yan ^{[1
]}

Hirota, Kaoru ^{[1
]}

机构：

[1] Tokyo Inst Technol, Midori Ku, G3-49,4259 Nagatsuta, Yokohama, Kanagawa 2268502, Japan

[2] Cent Inst Digital Res, Havana, Cuba

来源：

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2013年 / 17卷 / 02期

关键词：

classification; similarity; fuzzy inference; ECG; capnogram;

D O I：

10.20965/jaciii.2013.p0302

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 MIT-BIH 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.

引用

页码：302 / 310

页数：9

共 17 条

[1] Matching of quasi-periodic time series patterns by exchange of block-sorting signatures [J].