Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

@article{Faust2018AutomatedDO,
  title={Automated detection of atrial fibrillation using long short-term memory network with RR interval signals},
  author={Oliver Faust and Alex Shenfield and Murtadha Kareem and Ru San Tan and Hamido Fujita and U. Rajendra Acharya},
  journal={Computers in biology and medicine},
  year={2018},
  volume={102},
  pages={
          327-335
        }
}
Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep… CONTINUE READING
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