Key result
A fuzzy association rule mining method for ECG signals achieved 83.4% sensitivity, 80.7% specificity, and 81.4% accuracy for diagnosing myocardial ischemia.
Why the study?
Does a fuzzy association rule mining method accurately diagnose myocardial ischemia on ECG signals?
Does a fuzzy association rule mining method accurately diagnose myocardial ischemia on ECG signals?
A fuzzy association rule mining method provides interpretable rules for diagnosing myocardial ischemia on ECG with over 80% accuracy.
Should not change ECG interpretation in practice; leaves open prospective validation of fuzzy rules for ischemia.
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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Li et al. (2015) studied Myocardial ischemia. Fuzzy association rule mining based method was evaluated on Classification of myocardial ischemia and normal heartbeats. A fuzzy association rule mining method for ECG signals achieved 83.4% sensitivity, 80.7% specificity, and 81.4% accuracy for diagnosing myocardial ischemia.
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