2010 · 81 citations · 17 references
EngineeringBiomedical EngineeringElectrical EngineeringBiomedical Signal ProcessingElectrophysiologyMachine LearningAtrial FibrillationCardiac ArrhythmiasCardiac Signal ProcessingElectrocardiogram AnalysisElectrocardiographyVentricular TachycardiaCardiac Feature ExtractionCardiac Monitoring SystemsEcg Feature ExtractionElectrocardiogram DiagnosisElectrocardiogram Feature ExtractionFuzzy Inference SystemsIndependent Component AnalysisIndependent Component Analysis Features
This paper, presents an intelligent diagnosis system using hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) model for classification of Electrocardiogram (ECG) signals. Feature extraction using Independent Component Analysis (ICA) and Power spectrum, together with the RR interval then serve as input feature vector, this feature were used as input of ANFIS classifiers. six types of ECG signals they are normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC), Ventricular Tachycardia(VT), Ventricular Fibrillation (VF) and Supraventricular Tachycardia (SVT). The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy inference system. The results indicate a high level of efficient of tools used with an accuracy level of more than 97%. Keywords: ANFIS, adaptive neuro, fuzzy inference system, ECG, ICA, Power Spectral, RR-interval. 1.
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