Ensemble empirical mode decomposition of photoplethysmogram signals in biometric recognition

• المؤلفون الرئيسيون: Alonzo, Lea Monica B., Co, Homer S.
• التنسيق: text
• منشور في: Animo Repository 2019
• الموضوعات: Biometric identification; Plethysmography; Machine learning; Hilbert-Huang transform; Manufacturing
• الوصول للمادة أونلاين: https://animorepository.dlsu.edu.ph/faculty_research/2303; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3302/type/native/viewcontent

This research focuses on using photoplethysmogram (PPG) signals for biometric recognition. Specifically, the biometric traits studied are the ensemble empirical mode decomposition (EEMD) and power spectral density (PSD) of the PPG signals. The classifiers used for testing the performance of the algo...