Ischemia detection via ECG using ANFIS

Ali Gharaviri; Mohammad Teshnehlab; H A Moghaddam

doi:10.1109/IEMBS.2008.4649368

Ischemia detection via ECG using ANFIS

Annu Int Conf IEEE Eng Med Biol Soc. 2008:2008:1163-6. doi: 10.1109/IEMBS.2008.4649368.

Authors

Ali Gharaviri¹, Mohammad Teshnehlab, H A Moghaddam

Affiliation

¹ K.N. TOOSI University of Technology, Laboratory of Intelligent Systems. gharaviri@ieee.org

PMID: 19162871
DOI: 10.1109/IEMBS.2008.4649368

Abstract

An adaptive neuro-fuzzy interface system (ANFIS) classifier was used for automated detection of ischemic episodes resulting from ST-T segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular, the performance was measured in terms of beat by- beat ischemia detection and in terms of the detection of ischemic episodes. The algorithm used to cluster and then train the ANFIS classifier. The resulting ANFIS is capable of detecting ischemia independent of the lead used. It was found that the average ischemia episode detection sensitivity is 88.62% and specificity is 99.65%. This method can be used in electrocardiogram (ECG) processing in cases where reliable detection of ischemic episodes is desired as in the case of critical care units (CCUs).

Publication types

Evaluation Study
Research Support, Non-U.S. Gov't

MeSH terms

Diagnosis, Computer-Assisted / methods*
Electrocardiography / methods*
Fuzzy Logic*
Humans
Myocardial Ischemia / diagnosis*
Neural Networks, Computer*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity