@article{(Open Science Index):https://publications.waset.org/pdf/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	= {},
	  institution	= {},
	  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 Biomedical and Biological Engineering},
	  volume    = {10},
	  number    = {9},
	  year      = {2016},
	  pages     = {478 - 483},
	  ee        = {https://publications.waset.org/pdf/10005664},
	  url   	= {https://publications.waset.org/vol/117},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 117, 2016},