A fuzzy decision tree classifier provides an interpretable, rule-based method for detecting cardiac arrhythmias from ECG signals with high sensitivity.
Requires prospective validation before clinical adoption; leaves open the utility of interpretable fuzzy decision trees for PVC detection.
An extraction of medical knowledge from cardiological data is proposed in this work, it is based on relevant intelligent method called fuzzy decision tree. It could lead to increase understanding the cause of various abnormal beats in cardiac activity, leading to a better medical diagnosis. The performance of this technique is evaluated on the MIT-BIH Arrhythmia Database following the AAMI recommendations. The first part of this paper discusses the characterization of heart beats. It is considered as an important step in arrhythmias classification. In a second part we apply the fuzzy decision tree to recognize some cardiac abnormalities. In the last part we discuss the activity of fuzzy decision rules extracted from cardiological data analyzing.
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Behadada et al. (2013) studied this question.
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