Cardiac rhythms can be used to uniquely identify a person
the verdict
SUPPORTED
the evidence backs this
refutedsupported
the weight of evidence
8 sources for · 0 against
Multiple studies demonstrate that cardiac rhythms, such as electrocardiogram (ECG) and related cardiac motion signals, can be utilized with high accuracy to uniquely identify individuals.
Kusakunniran et al. (2023) review electrocardiogram (ECG) data acquisition methods and highlight the potential of using ECG as a biometric trait due to its uniqueness.
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The analysis
Numerous retrieved papers directly evaluate the use of cardiac rhythms (ECG and related cardiac signals) for biometric identification and person recognition, consistently demonstrating high accuracy across various machine learning architectures and acquisition techniques. There are no papers refuting the core claim.
Zhang et al. (2023) introduce a dynamic biometric system using heart sensors that achieves high classification accuracy based on interpersonal structural differences in cardiac motions.
Lee et al. (2024) propose an ensemble deep neural network for person identification using ECG signals acquired across different days with high accuracy.
Hossen et al. (2024) investigate the use of arrhythmic ECG signals for biometric recognition using deep learning, demonstrating successful person identification.
Carpio et al. (2025) discuss remote biometric collection frameworks centered on biophysical fluctuations unique to the person, including electrocardiographic data.
Wang et al. (2026) propose a radar-based framework for non-contact biometric identification through extracted heart signals, achieving high accuracy in recognizing individuals.