Knowledge-based and Data-driven Models in Arrhythmia Fuzzy Classification


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SILIPO, Rosaria, Rossano VERGASSOLA, Wei ZONG, Michael R. BERTHOLD, 2001. Knowledge-based and Data-driven Models in Arrhythmia Fuzzy Classification. In: Methods of Information in Medicine. 40(5), pp. 397-403. ISSN 0026-1270

@article{Silipo2001Knowl-24074, title={Knowledge-based and Data-driven Models in Arrhythmia Fuzzy Classification}, year={2001}, number={5}, volume={40}, issn={0026-1270}, journal={Methods of Information in Medicine}, pages={397--403}, author={Silipo, Rosaria and Vergassola, Rossano and Zong, Wei and Berthold, Michael R.} }

2013-07-15T06:25:03Z Zong, Wei 2001 Objectives: Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge- based and a data-driven fuzzy system applied to an electrocardiography classification problem.<br /><br /><br />Methods: For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures. A second set of fuzzy rules is automatically constructed on thirtynine MIT-BIH database’s records. The performances of the two classifiers on thirteen different records are comparable and up to a certain extent complementary. The two fuzzy models are then analyzed, by using the concept of information gain to estimate the impact of each ECG measure on each fuzzy decision process.<br /><br /><br />Results: Both systems rely on the beat prematurity degree and the QRS complex width and neglect the P wave existence and the ST segment features. The PR interval is not well characterized across the fuzzy medical rules while it plays an important role in the data-driven fuzzy system. The T wave area shows a higher information gain in the knowledge based decision process, and is not very much exploited by the data-driven system.<br /><br /><br />Conclusions: The main difference between a human designed and a data driven ECG arrhythmia classifier is found about the PR interval and the T wave. terms-of-use 2013-07-15T06:25:03Z Vergassola, Rossano Berthold, Michael R. Silipo, Rosaria Vergassola, Rossano Berthold, Michael R. eng Methods of Information in Medicine ; 40 (2001), 5. - S. 397-403 Silipo, Rosaria Knowledge-based and Data-driven Models in Arrhythmia Fuzzy Classification Zong, Wei

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