Ensemble empirical mode decomposition of photoplethysmogram signals in biometric recognition

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Conference Proceeding

Source Title

2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019

First Page

255

Last Page

259

Publication Date

7-1-2019

Abstract

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 algorithm were K-nearest neighbors algorithm (KNN), support vector machine (SVM), and random forest (RF). Training, testing, and k-fold cross validation were done using data from public database. PPG was found to be suitable for biometric recognition, although with weakness that may be addressed through gathering and training of larger sets of data. © 2019 IEEE.

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Digitial Object Identifier (DOI)

10.1109/ACIRS.2019.8935943

Disciplines

Manufacturing

Keywords

Biometric identification; Plethysmography; Machine learning; Hilbert-Huang transform

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