Electrocardiogram Feature Extraction and Classification: Survey |
Author(s): |
| Thripurna Thatipelli , GRIET; Padmavathi Kora, GRIET |
Keywords: |
| ECG, DWT, LDA, PCA, ICA, ANN, SVM |
Abstract |
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the analysis of ECG signal plays a significant role in detecting cardiac peculiarities. ECG signal get effected by presence of noise, added from different sources. Several types of noises are present in the signal by Electrodes, Power interference, Instruments. It is absolutely essential to diminish these disturbances in ECG signal to increase accuracy and reliability. This paper describes various methods for feature extraction such as Discrete Wavelet (DWT), linear methods such as Linear discriminant Analysis (LDA), Principal Component Analysis (PCA), Independent Component Analysis (ICA) and nonlinear methods. The classification mostly based on Artificial Neural Networks (ANN), Support Vector Machines (SVM).Each method have their advantage and Limitations. |
Other Details |
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Paper ID: IJSRDV5I80320 Published in: Volume : 5, Issue : 8 Publication Date: 01/11/2017 Page(s): 235-238 |
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