Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification

@article{Vijayarangan2020InterpretingDN,
  title={Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification},
  author={Sricharan Vijayarangan and Balamurali Murugesan and Vignesh Ravichandran and P. PreejithS. and J. Joseph and M. Sivaprakasam},
  journal={2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
  year={2020},
  pages={300-303}
}
  • Sricharan Vijayarangan, Balamurali Murugesan, +3 authors M. Sivaprakasam
  • Published 2020
  • Computer Science, Engineering, Medicine
  • 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
  • Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is performed by examination of the Electrocardiogram (ECG) by a cardiologist. This method of diagnosis is hampered by the lack of accessibility to expert cardiologists. For quite some time, signal processing methods had been used to automate arrhythmia diagnosis… CONTINUE READING
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