An Efficient Method Of ECG Beats Feature Extraction/Classification With Multiclass SVM Error Correcting Output Codes | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 5, Volume 28, Issue 2, July 2019, Page 65-78 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.62765 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Salma El-Soudy* 1; Ayman El-Sayed 1; Adnan Khalil2; Irshad Khalil3; Taha E. Taha4; Fathi Abd El-Samie4
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| 1Computer Science & Eng. Dept., Faculty of Electronic Eng., Menoufia University. | ||||
| 2Department of Computer Science, University of Malakand, Pakistan | ||||
| 3Department of Computer Science, University of Malakand, Pakistan. | ||||
| 4Electronic & Comm. Eng. Dept., Faculty of Electronic Eng., Menoufia University. | ||||
| Abstract | ||||
| This paper presents an efficient algorithm for classifying the ECG beats to the main four types. These types are normal beat (normal), Left Bundle Branch Block beats (LBBB), Right Bundle Branch Block beats (RBBB), Atrial Premature Contraction (APC). Feature extraction is performed from each type using Legendre moments as a tool for characterizing the signal beats. A Multiclass Support Vector Machine (multiclass SVM) is used for the classification on process with Legendre polynomial coefficients as inputs. A comparison study is presented between the proposed and some existing approaches. Simulation results reveal that the proposed approach gives 97.7% accuracy levels compared to 95.7447%, 95.88%, 95.03% , 93.40%, 96.02%, 95.95%, 96.24% achieved with Discrete wavelet (DWT), Haar wavelet and principle component analysis (PCA) as feature extractors and ANN, Simple Logic Random Forest, LibSVM and J48 as classifiers. | ||||
| Keywords | ||||
| Legendre Polynomials; Shifted Legendre Polynomials; classification; Multiclass Support Vector Machine | ||||
| References | ||||
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