ArticlePloS one2013
Normalizing electrocardiograms of both healthy persons and cardiovascular disease patients for biometric authentication.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Robustness of convolutional neural networks to physiological electrocardiogram noise.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography.Sensors (Basel, Switzerland) · 2020Article
- Ratiometric Impedance Sensing of Fingers for Robust Identity Authentication.Scientific reports · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although electrocardiogram (ECG) fluctuates over time and physical activity, some of its intrinsic measurements serve well as biometric features. Considering its constant availability and difficulty in being faked, the ECG signal is becoming a promising factor for biometric authentication. The majority of the currently available algorithms only work well on healthy participants. A novel normalization and interpolation algorithm is proposed to convert an ECG signal into multiple template cycles, which are comparable between any two ECGs, no matter the sampling rates or health status. The overall accuracies reach 100% and 90.11% for healthy participants and cardiovascular disease (CVD) patients, respectively.
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Registered trials
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