Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

@article{Acir2005ClassificationOE,
  title={Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm},
  author={Nurettin Acir},
  journal={Neural Computing & Applications},
  year={2005},
  volume={14},
  pages={299-309}
}
In this paper, we present a new system for the classification of electrocardiogram (ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by… CONTINUE READING
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