Feature selection for ECG signal processing using improved genetic algorithm and empirical mode decomposition |
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| Affiliation: | 1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China;2. Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;1. Dept. of Mechanical Engineering, University of Canterbury, Christchurch 8140, New Zealand;2. Dept. of Orthopaedic Surgery and Musculo-Skeletal Medicine, University of Otago Christchurch, New Zealand;1. Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India;2. Department of Project Management, National Institute of Industrial Engineering, Mumbai, India;3. Department of Civil Engineering, Indian Institute of Technology, Madras, India;4. Department of Cardiology, National Heart Care Centre Singapore, Singapore;5. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore;6. Department of Biomedical Engineering, School of Science and Technology, SUSS, Singapore;7. International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan;1. School of Management, Huazhong University of Science and Technology, China;2. School of Management, Xi’an Jiaotong Univesity, China;3. Department of Otorhinolaryngology-Head & Neck Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, China |
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| Abstract: | This paper proposes a novel scheme of feature selection, which employs a modified genetic algorithm that uses a variable-range searching strategy and empirical mode decomposition (EMD). Combined with support vector machines (SVMs), a new pattern recognition method for electrocardiograph (ECG) is developed. First, the ECG signal is decomposed into intrinsic mode functions (IMFs) that represent signal characteristics with sample oscillatory modes. Then, the modified genetic algorithm with variable-range encoding and dynamic searching strategy is used to optimize statistical feature subsets. Next, a statistical model based on receiver operating characteristic (ROC) analysis is developed to select the dominant features. Finally, the SVM-based pattern recognition model is used to classify different ECG patterns. Comparative studies with peer-reviewed results and two other well-known feature selection methods demonstrate that the proposed method can select dominant features in processing ECG signal, and achieve better classification performance with lower feature dimensionality. |
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| Keywords: | Feature selection Genetic algorithms Empirical mode decomposition ECG signal processing |
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