Document Type : Full Research Paper

Authors

• Jamal Esmaeilpour ¹
• Sattar Mirzakouchaki ²
• Jalil Seyfali Harsini ³
• Abdorrahim Kadkhoda Mohammadi ¹

¹ Instructor, Electrical Group, Bookan Branch, Islamic Azad University

² Associate Professor, Electrical Engineering School, Iran University of Science and Technology

³ Instructor, Engineering Faculty- Electrical Group, Gilan University

Abstract

In this paper, the role of Vector Quantizer Neural Network in classification of six types of ECG signals has been investigated using the features that extracted from Daubechies6 Wavelet transformation. The six types of signals are: normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction paced beat and fusion of paced and normal beats. The required data were obtained from the MIT/BIH arrhythmia databases. By using the annotation files of the databases, the patterns of these six types of ECG signals were separated. Then, for better feature extraction, filtering and scaling on the patterns were applied. We used the energies of the last five detailed signals obtained from the exerting the Wavelet transformation in six levels, as the pattern features for Vector Quantizer Network training and testing. From each class, five hundred patterns were used for network training and one hundred patterns for testing. The results indicated %93.1 accuracy for six classes and above %94.3 for lesser than six classes. Then the rate of similarity and dissimilarity of the classes were considered. Finally, the results of this method were compared with some other methods in terms of accuracy.

Keywords

• Electrocardiogram
• Cardiac Arrhythmias
• Classification
• Vector Quantizer Neural Network
• Wavelet Transformation

Main Subjects

• Cardiovascular Biomechanics
• Biomedical Image Processing / Medical Image Processing