An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases |
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| Authors: | Engin Avci Ibrahim Turkoglu |
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| Affiliation: | 1. Department of Cardiology, Fiona Stanley Hospital, Perth, Australia;2. Heart Care, Perth, Australia;1. Aix-Marseille Université, CNRS, ISM UMR 7287, Marseille, France;2. Protomedlabs, Marseille, France;3. Québec Heart and Lung Institute, Laval University, Québec, Québec, Canada;4. Laboratory of Cardiovascular Fluid Dynamics, Concordia University, Montréal, Québec, Canada;1. Department of Electronic, Information and Bioengineering, Politecnico di Milano, via Golgi 39, Milan 20133, Italy;2. ForCardioLab, Fondazione per la Ricerca in Cardiochirurgia ONLUS, Milan, Italy;3. Cardiovascular Surgery Department, L. Sacco Hospital, University of Milan, Milan, Italy;1. Biophysics laboratory LTIM-LR12ES06, faculty of medicine of Monastir, university of Monastir, 5019 Monastir, Tunisia;2. CRISTAL laboratory, ENSI, research group in forms and images of Tunisia (GRIFT), university of Manouba, 2010 Manouba, Tunisia;1. Department of Cardiology, Linkoping University, Linkoping, Sweden;2. Department of Physiology, Linkoping University, Linkoping, Sweden;3. Department of Cardiothoracic Surgery, Linkoping University, Linkoping, Sweden;4. Institution of Medical and Health Sciences, Linkoping, Sweden |
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| Abstract: | In this paper, an intelligent diagnosis system based on principle component analysis (PCA) and adaptive network based on fuzzy inference system (ANFIS) for the heart valve disease is introduced. This intelligent system deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler ultrasound (DHS). Here, the wavelet entropy is used as features. This intelligent system has three phases. In pre-processing phase, the data acquisition and pre-processing for DHS signals are performed. In feature extraction phase, the feature vector is extracted by calculating the 12 wavelet entropy values for per DHS signal and dimension of Doppler signal dataset, which are 12 features, is reduced to 6 features using PCA. In classification phase, these reduced wavelet entropy features are given to inputs ANFIS classifier. The correct diagnosis performance of the PCA–ANFIS intelligent system is calculated in 215 samples. The classification accuracy of this PCA–ANFIS intelligent system was 96% for normal subjects and 93.1% for abnormal subjects. |
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