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the claim

Cardiac rhythms can be used to uniquely identify a person

the verdict
SUPPORTED
the evidence backs this
Recorded sources
8 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that cardiac rhythms can be used to uniquely identify a person?

Yes. 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.

Verdict: SUPPORTED · 8 sources for, 0 against · Last checked

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.

Evidence for · 8
Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods.
2023
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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TMC, P., RC, C., V, S., & R, S. (2023). Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods.. doi.org. https://doi.org/10.3390/s23031507

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More for · 7
Heart ID: Biometric Identification Using Wearable MIMO RF Heart Sensors
2023
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.
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Conroy, T., Hui, X., Sharma, P., & Kan, E. (2023). Heart ID: Biometric Identification Using Wearable MIMO RF Heart Sensors. doi.org. https://doi.org/10.1109/JERM.2022.3223034

ECG-based Biometric Recognition without QRS Segmentation: A Deep Learning-Based Approach
2021
Fan et al. (2021) propose a deep learning-based ECG biometric identification scheme that achieves high identification rates across multiple enrollees.
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Chiu, J., Chang, C., & Wu, S. (2021). ECG-based Biometric Recognition without QRS Segmentation: A Deep Learning-Based Approach. doi.org. https://doi.org/10.1109/EMBC46164.2021.9630899

Single-Lead ECG Cross-Session Identification Based on Conditional Domain Adversarial Network
2024
Cai et al. (2024) develop a robust ECG identification model using single heartbeat signals to achieve high cross-session identification accuracy.
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Chen, X., Shen, Y., & Chi, T. (2024). Single-Lead ECG Cross-Session Identification Based on Conditional Domain Adversarial Network. doi.org. https://doi.org/10.1109/JSEN.2024.3386214

An Ensemble Deep Neural Network-Based Method for Person Identification Using Electrocardiogram Signals Acquired on Different Days
2024
Lee et al. (2024) propose an ensemble deep neural network for person identification using ECG signals acquired across different days with high accuracy.
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Byeon, Y. & Kwak, K. (2024). An Ensemble Deep Neural Network-Based Method for Person Identification Using Electrocardiogram Signals Acquired on Different Days. doi.org. https://doi.org/10.3390/app14177959

Person identification with arrhythmic ECG signals using deep convolution neural network.
2024
Hossen et al. (2024) investigate the use of arrhythmic ECG signals for biometric recognition using deep learning, demonstrating successful person identification.
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A, A., S, A., S, I., & AM, A. (2024). Person identification with arrhythmic ECG signals using deep convolution neural network.. doi.org. https://doi.org/10.1038/s41598-024-55066-w

Setting Up Our Lab-in-a-Box: Paving the Road Towards Remote Data Collection for Scalable Personalized Biometrics.
2025
Carpio et al. (2025) discuss remote biometric collection frameworks centered on biophysical fluctuations unique to the person, including electrocardiographic data.
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M, E., J, R., J, V., & EB, T. (2025). Setting Up Our Lab-in-a-Box: Paving the Road Towards Remote Data Collection for Scalable Personalized Biometrics.. doi.org. https://doi.org/10.3390/jpm15100463

Beyond Contact: An Open-Set Biometric Identification System Using Radar-Extracted Heart Signals.
2026
Wang et al. (2026) propose a radar-based framework for non-contact biometric identification through extracted heart signals, achieving high accuracy in recognizing individuals.
Get citation
Z, X., M, B., & T, O. (2026). Beyond Contact: An Open-Set Biometric Identification System Using Radar-Extracted Heart Signals.. doi.org. https://doi.org/10.1109/jbhi.2025.3608801

What does the evidence say?

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.

Receipt History
first checked04 Aug 2026
judged → SUPPORTED · 8204 Aug 2026
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