Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/10005664,
  title    = {Heart-Rate Resistance Electrocardiogram Identification Based on Slope-Oriented Neural Networks},
  author    = {Tsu-Wang Shen and  Shan-Chun Chang and  Chih-Hsien Wang and  Te-Chao Fang},
  country   = {Taiwan},
  institution={Tzu Chi University},
  abstract  = {For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.},
    journal   = {International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering},  volume    = {10},
  number    = {9},
  year      = {2016},
  pages     = {478 - 483},
  ee        = {http://waset.org/publications/10005664},
  url       = {http://waset.org/Publications?p=117},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 117, 2016},
}