ECG Biometric Recognition: A Comparative Analysis

By Ikenna Odinaka, Po-Hsiang Lai, A. D. Kaplan, J. O’Sullivan, E. Sirevaag, J. Rohrbaugh. IEEE Transactions on Information Forensics and Security, 2012

373 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/61566dcd32e8e724353b95a469bf5849904a1058

How researchers see this paper

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

1. R. Sassi
2. F. Scotti
3. Patricia Ordóñez de Pablos
4. Vincenzo Piuri
5. Wadee Alhalabi Alhalabi
6. Sally Shuk Han Pang
7. Kwok Tai Chui
8. Ryan Wen Liu

Citations in the corpus

• Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, background): “In [89], it was concluded that there are three types of features namely fiducial features (FF), non-fiducial features (NFF) and hybrid features (HF).”
• Deep-ECG: Convolutional Neural Networks for ECG biometric recognition (2019, methodology): “Previous studies that 8 heartbeats are a balanced trade-o ff between and usability [2, 31].”