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

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.