Ensemble empirical mode decomposition of photoplethysmogram signals in biometric recognition

• Main Authors: Alonzo, Lea Monica B., Co, Homer S.
• Format: text
• Published: Animo Repository 2019
• Subjects: Biometric identification; Plethysmography; Machine learning; Hilbert-Huang transform; Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2303; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3302/type/native/viewcontent
• Institution: De La Salle University

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...