Key result
Hedge Algebra Type-2 Fuzzy System achieves ~2% error in arrhythmia classification, outperforming MLP and TSK.
The proposed HAT2 Fuzzy System provides a highly accurate and computationally simple method for multi-class ECG arrhythmia recognition, making it suitable for IoT and portable healthcare devices.
May enable arrhythmia detection on portable IoT devices; leaves open prospective clinical validation before practice change.
The paper presents a new application of Hedge Algebra in Type 2 (HAT2) Fuzzy System (FS) for electrocardiographical signal recognition and classification. The HAT2 integrates the capability of fuzzy logic in modeling uncertainties of data and the linguistic variables to describe and to manipulate the human knowledge. The proposed approach will be tested with different types of arrhythmia in the ECG signals taken from the MIT - BIH (Massachusetts Institute of Technology and Boston's Beth Israel Hospital) Arrhythmia Databases. The numerical results will be compared with other methods to show the high quality of proposed solution. The proposed solution is also simple with low complexity of computation, which makes it suitable for use in IoT or portable systems.
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Hoai-Nam Tran (2023) studied Arrhythmia (n=19). Hedge Algebra in Type 2 (HAT2) Fuzzy System vs. MLP, TSK, and SVM classifiers was evaluated on Classification error rate for 7 types of rhythms. The Hedge Algebra Type-2 Fuzzy System achieved a classification error of 2.12% for a 7-class arrhythmia recognition problem, outperforming MLP and TSK classifiers.
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