ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix

@article{Zhang2014ECGQA,
  title={ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix},
  author={Ya-tao Zhang and Cheng-yu Liu and Shou-shui Wei and Chang-zhi Wei and Fei-fei Liu},
  journal={Journal of Zhejiang University SCIENCE C},
  year={2014},
  volume={15},
  pages={564-573}
}
We propose a systematic ECG quality classification method based on a kernel support vector machine (KSVM) and genetic algorithm (GA) to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain… CONTINUE READING
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