title: An adaptive deep learning approach for PPG-based identification.
abstract: Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.
authors.forename: V
authors.initials: V
authors.lastname: Jindal
authors.forename: J
authors.initials: J
authors.lastname: Birjandtalab
authors.forename: M Baran
authors.initials: MB
authors.lastname: Pouyan
authors.forename: M
authors.initials: M
authors.lastname: Nourani
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D056667:Biometric Identification; D006801:Humans; D008297:Male; D017156:Photoplethysmography / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult