%0 Journal Article
	%A Tsu-Wang Shen and  Shan-Chun Chang and  Chih-Hsien Wang and  Te-Chao Fang
	%D 2016
	%J International Journal of Biomedical and Biological Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 117, 2016
	%T Heart-Rate Resistance Electrocardiogram Identification Based on Slope-Oriented Neural Networks
	%U https://publications.waset.org/pdf/10005664
	%V 117
	%X 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.
	%P 478 - 483