Abstract
A method based on fuzzy association rule mining is proposed for ischemia diagnosis on ECG signal. The proposal provides interpretable and understandable information to doctors as a reference, while rule mining on fuzzy itemsets guarantees that features segmentation before rule extraction is feasible and effective. A set of fuzzy association rules are mined through experiments on the data from European ST-T database, and classification results of ischemia and normal beats using extracted rules obtain values of 83.4%, 80.7%, and 81.4% for sensitivity, specificity, and accuracy, respectively. The proposal aims to become a helpful tool to accelerate the diagnosis of ischemia on ECG signal, and to be expanded to other heart disease diagnosis areas such as hypertensive heart disease and arrhythmia.
| Original language | English |
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| Pages | 69-74 |
| Number of pages | 6 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | Joint International Conference of the 10th China-Japan International Workshop on Information Technology and Control Applications and the 6th International Symposium on Computational Intelligence and Industrial Applications, ITCA and ISCIIA 2014 - Changsha, China Duration: 15 Sept 2014 → 20 Sept 2014 |
Conference
| Conference | Joint International Conference of the 10th China-Japan International Workshop on Information Technology and Control Applications and the 6th International Symposium on Computational Intelligence and Industrial Applications, ITCA and ISCIIA 2014 |
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| Country/Territory | China |
| City | Changsha |
| Period | 15/09/14 → 20/09/14 |
Keywords
- ECG
- Fuzzy association rule mining
- Heart disease
- Ischemia diagnosis
- Time series data