Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal
Tianyu Li*, Fangyan Dong**, and Kaoru Hirota*
*Department of Computational Intelligence & Systems Science, Tokyo Institute of Technology
G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**Education Academy of Computational Life Sciences, Tokyo Institute of Technology
J3-141, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
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.
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