Key result
The proposed fuzzy expert system methodology escalated classification performance compared to the simple initial crisp model for automated arrhythmic and ischemic beat classification.
An automated fuzzy expert system improves ECG beat classification performance compared to crisp models while remaining interpretable.
May support interpretable automated ECG classification tools; leaves open prospective clinical validation before practice change.
A methodology for the automated development of fuzzy expert systems is presented. The idea is to start with a crisp model described by crisp rules and then transform them into a set of fuzzy rules, thus creating a fuzzy model. The adjustment of the model's parameters is performed via a stochastic global optimization procedure. The proposed methodology is tested by applying it to problems related to cardiovascular diseases, such as automated arrhythmic beat classification and automated ischemic beat classification, which, besides being well-known benchmarks, are of particular interest due to their obvious medical diagnostic importance. For both problems, the initial set of rules was determined by expert cardiologists, and the MIT-BIH arrhythmia database and the European ST-T database are used for optimizing the fuzzy model's parameters and evaluating the fuzzy expert system. In both cases, the results indicate an escalation of the performance from the simple initial crisp model to the more sophisticated fuzzy models, proving the scientific added value of the proposed framework. Also, the ability to interpret the decisions of the created fuzzy expert systems is a major advantage compared to "black box" approaches, such as neural networks and other techniques.
No takes yet. Share an insight, caveat, or question.
Tsipouras et al. (2007) studied Cardiovascular diseases (arrhythmic and ischemic beat classification). Automated development of fuzzy expert systems vs. Simple initial crisp model was evaluated on Classification performance. The proposed fuzzy expert system methodology escalated classification performance compared to the simple initial crisp model for automated arrhythmic and ischemic beat classification.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: