ECG to identify individuals
By Steven A. Israel, John M. Irvine, Andrew Cheng, Mark D. Wiederhold, Brenda K. Wiederhold. Pattern Recognition, 2005
749 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/04e8648b268ffe16b0f0eab68402bdb433708b83
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
In Cognitive Neuroscience
- Hugo Plácido da Silva
- André Lourenço
- K. Plataniotis
- Dimitrios Hatzinakos
- Ana L. N. Fred
- Foteini Agrafioti
Citations in the corpus
- 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.”
- A review of biometric technology along with trends and prospects (2014, result): “Furthermore, some studies based on the analysis of cepstral coefficients of heart sound and analysis of fiducial points from ECG data displayed promising results towards human identification [189, 190, 191, 192].”
- Driver Fatigue Detection Systems: A Review (2019, background): “Among all biological features investigated (in this study), ECG can be measured in a less intrusive method; however ECG are interpersonal and show much diversity for individual to individual that it can be used for human identification [101], [102].”
- Unveiling the Biometric Potential of Finger-Based ECG Signals (2011, methodology): “Therefore, the application of ECG for biometric purposes has been studied for long, both under controlled and unrestrained scenarios [2–5].”
- ECG Biometric Recognition: A Comparative Analysis (2012, methodology): “3) LDA Classifiers: Classification based on linear discriminant analysis, a special case of generative model classifiers (GMCs), has been used by several studies in the ECG biometrics literature [6], [7], [21], [22], [25], [34], [35], [42].”
- Deep-ECG: Convolutional Neural Networks for ECG biometric recognition (2019, neutral): “7 Morphology + PCA [21] 20 95 Morphology [21] 29 100 Heartbeat selection [41] 74 98.”