@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}, }