Verification based ECG biometrics with cardiac irregular conditions using heartbeat level and segment level information fusion

Published

Conference Paper

We propose an ECG based robust human verification system for both healthy and cardiac irregular conditions using the heartbeat level and segment level information fusion. At the heartbeat level, we first propose a novel beat normalization and outlier removal algorithm after peak detection to extract normalized representative beats. Then after principal component analysis (PCA), we apply linear discriminant analysis (LDA) and within-class covariance normalization (WCCN) for beat variability compensation followed by cosine similarity and Snorm as scoring. At the segment level, we adopt the hierarchical Dirichlet process auto-regressive hidden Markov model (HDP-AR-HMM) in the Bayesian non-parametric framework for unsupervised joint segmentation and clustering without any peak detection. It automatically decodes each raw signal into a string vector. We then apply n-gram language model and hypothesis testing for scoring. Combining the aforementioned two subsystems together further improved the performance and outperformed the PCA baseline by 25% relatively on the PTB database. © 2014 IEEE.

Full Text

Duke Authors

Cited Authors

  • Li, M; Li, X

Published Date

  • January 1, 2014

Published In

Start / End Page

  • 3769 - 3773

International Standard Serial Number (ISSN)

  • 1520-6149

International Standard Book Number 13 (ISBN-13)

  • 9781479928927

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2014.6854306

Citation Source

  • Scopus