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A fuzzy association rule mining based method is proposed for myocardial  ischemia diagnosis on ECG signals. The proposal provides interpretable and  understandable information to doctors as an assistant reference, while rule  mining on fuzzy itemsets guarantees that the feature segmentation before  rule extraction is feasible and effective. A set of fuzzy association rules  is mined through experiments on data from the European ST-T Database, and  classification results of myocardial ischemia and normal heartbeats on the  test dataset using the extracted rules obtained values of 83.4%,  80.7%, and 81.4% for sensitivity, specificity, and accuracy,  respectively. The proposed method aims to become a helpful tool to  accelerate the diagnosis of myocardial ischemia on ECG signal, and to be  expanded to other heart disease diagnosis areas such as hypertensive heart  disease and arrhythmia.

Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal