Accurate Arrhythmia classification using auto-associative neural network

@article{Chakroborty2013AccurateAC,
  title={Accurate Arrhythmia classification using auto-associative neural network},
  author={Sandipan Chakroborty},
  journal={2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2013},
  pages={4247-4250}
}
Currently about one in eighteen of the American population suffer from cardiac Arrhythmias that lead to Coronary Heart Diseases and this rate is steadily increasing. An early monitoring and diagnosis of Arrhythmia based on Electrocardiogram signals can help in reducing mortality. This paper primarily focuses on the application of Auto Associative Neural Network as a new classification approach, which does not require feature extraction task. The weights of a trained Neural Network are stored as… CONTINUE READING