Ischemia detection via ECG using ANFIS

Author

Gharaviri, Ali ; Teshnehlab, Mohammad ; Moghaddam, H.A.

Author_Institution

K.N. TOOSI University of Technology, Laboratory of Intelligent Systems, Iran

fYear

2008

fDate

20-25 Aug. 2008

Firstpage

1163

Lastpage

1166

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).

Keywords

Adaptive systems; Clustering algorithms; Databases; Digital filters; Electrocardiography; Electrodes; Filtering; Injuries; Ischemic pain; Particle measurements; Diagnosis, Computer-Assisted; Electrocardiography; Fuzzy Logic; Humans; Myocardial Ischemia; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Conference_Location

Vancouver, BC

ISSN

1557-170X

Print_ISBN

978-1-4244-1814-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2008.4649368

Filename

4649368