Sadeghi, Z., Jazayeriy, H., Fateri, S. (2016). A Low Complexity ANFIS Approach for Premature Ventricular Contraction Detection Based on Backward Elimination. Journal of Advances in Computer Research, 7(1), 35-48.
Zahra Sadeghi; Hamid Jazayeriy; Soheil Fateri. "A Low Complexity ANFIS Approach for Premature Ventricular Contraction Detection Based on Backward Elimination". Journal of Advances in Computer Research, 7, 1, 2016, 35-48.
Sadeghi, Z., Jazayeriy, H., Fateri, S. (2016). 'A Low Complexity ANFIS Approach for Premature Ventricular Contraction Detection Based on Backward Elimination', Journal of Advances in Computer Research, 7(1), pp. 35-48.
Sadeghi, Z., Jazayeriy, H., Fateri, S. A Low Complexity ANFIS Approach for Premature Ventricular Contraction Detection Based on Backward Elimination. Journal of Advances in Computer Research, 2016; 7(1): 35-48.
A Low Complexity ANFIS Approach for Premature Ventricular Contraction Detection Based on Backward Elimination
1Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran
2Faculty of Electrical and Computer Engineering, Noshirvani University of Technology, Babol, Iran
3Faculty of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran
Abstract
Premature ventricular contraction (PVC) is one of the common cardiac arrhythmias. The occurrence of PVCis dangerous in people who have recently undergone heart. A PVC beatcan easily be diagnosed by a doctor based on the shape of the electrocardiogram signal. But in automatic detection, extracting several important features from each beat is required. In this paper, a method for automatic detection of PVC using adaptive neuro-fuzzy inference systems (ANFIS) is presented. In the proposed model first feature selection has been done using backward elimination algorithm, and then an ANFIS has been trained with selected attributes. The performance of the proposed method has been compared with two other methods. Simulation results show that the proposed algorithm, in addition to maintaining the classification accuracy compared to existing methods uses fewer features and requires less computing time, which is suitable for implementation on hardware with limited processing capability.
Top