Ischemia detection via ECG using ANFIS.
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).
Gharaviri
Ali
A
K.N. TOOSI University of Technology, Laboratory of Intelligent Systems. gharaviri@ieee.org
Teshnehlab
Mohammad
M
Moghaddam
H A
HA
D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D017202:Myocardial Ischemia / Q000175:diagnosis*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity