Heartbeats Do Not Make Good Pseudo-Random Number Generators: An Analysis of the Randomness of Inter-Pulse Intervals

No prior work had resolved the true entropy potential of human cardiac rhythms for secure data encryption. That uncertainty drove the need for rigorous statistical validation of biometric signal sources. It was already known that wearable medical devices require robust authentication mechanisms to protect sensitive patient information. Researchers previously suggested that the timing between consecutive heartbeats might serve as a viable foundation for generating cryptographic keys. This gap motivated a comprehensive evaluation of whether these biological signals possess sufficient unpredictability. Prior research has shown that various authentication protocols rely on this assumption without extensive empirical verification. The field lacked a large-scale analysis to confirm if these physiological patterns meet the strict requirements for random number generation. This study addresses the discrepancy between theoretical proposals and the practical statistical properties of cardiac data.

The aim of this study is to determine if the timing between heartbeats can serve as a reliable source of entropy for cryptographic applications. Researchers sought to investigate the widespread assumption that these biological signals are suitable for secure biometric authentication. The project addresses the growing concern regarding the security of wearable and implantable medical devices. Many existing protocols rely on the unpredictability of cardiac rhythms without sufficient empirical validation. This study was motivated by the need to verify whether such physiological data meets the rigorous standards required for generating secure keys. The authors identified a gap in the literature where theoretical proposals lacked large-scale statistical evidence. By analyzing a massive collection of public data, the team intended to resolve the uncertainty surrounding this biometric approach. This work provides a critical assessment of the feasibility of using cardiac signals for future security schemes.

The review approach involved a large-scale statistical examination of 19 public electrocardiogram datasets. Researchers accessed these files through the Physionet repository to ensure a diverse and representative sample size. The team processed signals from 1353 distinct subjects to evaluate the consistency of their findings. They extracted the timing between consecutive heartbeats to form the primary data for analysis. A standard battery of randomness tests was then applied to these extracted values. This systematic procedure allowed the investigators to quantify the entropy present within the cardiac rhythms. The design focused on comparing these biological measurements against established mathematical criteria for unpredictability. This rigorous methodology ensured that the evaluation remained independent of specific device hardware or proprietary software constraints.

The key findings from the literature indicate that inter-pulse intervals do not function as a high-quality source of randomness. Statistical analysis of the 1353 subjects revealed significant patterns that contradict the assumption of unpredictability. The tests consistently showed that these biological signals fail to meet the standards required for cryptographic purposes. This result holds true across the various sampling frequencies and recording lengths present in the public datasets. The data suggests that the entropy levels are insufficient for generating secure keys in modern authentication protocols. These findings directly challenge the validity of previous research that proposed using cardiac timing for security. The study provides evidence that these signals contain predictable structures that could be exploited. Consequently, the reliance on such metrics for encryption is statistically unsupported by the observed data.

The authors demonstrate that heartbeat timing fails to provide a reliable source of entropy for cryptographic applications. Their findings suggest that current authentication protocols relying on this biological signal may be inherently insecure. The researchers propose that designers must reconsider the use of these intervals for generating secure keys. This synthesis indicates that physiological signals do not inherently possess the statistical complexity required for high-level security. The study implies that future schemes should avoid relying solely on these cardiac metrics for randomness. These results highlight a significant limitation in existing biometric security frameworks. The authors conclude that further exploration is needed to identify more robust alternatives for device authentication. Their work serves as a warning against the unverified adoption of biological rhythms in cryptographic system design.