One-lead ECG for identity verification

By T. Shen, Willis J. Tompkins, Y. Hu. Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology, 2002

335 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Information Systems

View Paper: https://www.semanticscholar.org/paper/57f6a056e5532d1cd7e098607e1675f7dc58c7e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Hugo Plácido da Silva
2. André Lourenço
3. Ana L. N. Fred

In Cognitive Neuroscience

1. Hugo Plácido da Silva
2. André Lourenço
3. K. Plataniotis
4. Dimitrios Hatzinakos
5. Ana L. N. Fred
6. Foteini Agrafioti

In Information Systems

1. Hugo Plácido da Silva
2. André Lourenço
3. K. Plataniotis
4. Ana L. N. Fred
5. Yongjin Wang

Citations in the corpus

• ECG Biometric Recognition: A Comparative Analysis (2012, methodology): “the P, R, and T waves, the temporal distance between wave boundaries (onset and offset of the P, Q, R, S, and T waves), the area of the waves, and slope information [5], [6], [20], [21],”
• Unveiling the Biometric Potential of Finger-Based ECG Signals (2011, methodology): “In the literature, there are several approaches for ECG feature extraction: fiducial [3–5, 8] and nonfiducial [6, 11].”
• Analysis of Human Electrocardiogram for Biometric Recognition (2008, methodology): “Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points.”