Abstract
Verifiable physiological reasoning begins with a measurable claim whose inputs, units, and transformations can be replayed. This secondary analysis examines that requirement using reference annotations from MIT-BIH Arrhythmia Database record 100. Of 2,274 annotation entries, 2,273 identify beats; restricting intervals to consecutive normal-labeled beats yields 2,204 intervals. Their median is 797.222 milliseconds. An ordinary least squares projection of interval duration on recording time gives a slope of 0.108 milliseconds per minute, with a Newey-West 95 percent interval from -0.453 to 0.669. This small linear association does not establish physiological stationarity or clinical normality. The analysis uses annotations rather than newly detected waveform events and evaluates no foundation model or language agent. It develops a concrete measurement ledger connecting a published waveform-reasoning position with the evidence that a future model would need to preserve, inspect, and verify.
References
Wang, Xiaoda, et al. “Position: Beyond Prediction: Toward Verifiable Physiological Waveform Reasoning with Foundation Models and Agentic LLMs.” Proceedings of the 43rd International Conference on Machine Learning, PMLR, vol. 306, 2026, pp. 172441-172459. https://proceedings.mlr.press/v306/wang26lz.html.
Tong, H., Xu, G., & Chen, Y. (2026, May). Hadamard Tensor Ring for Efficient Low-Rank Fine-Tuning. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Moody, George, and Roger Mark. “MIT-BIH Arrhythmia Database.” Version 1.0.0, PhysioNet, 2005. https://doi.org/10.13026/C2F305.
Moody, G.B. and R.G. Mark. “The impact of the MIT-BIH Arrhythmia Database.” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, 2001, pp. 45-50. https://doi.org/10.1109/51.932724.
Newey, Whitney K., and Kenneth D. West. “A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica, vol. 55, no. 3, 1987, pp. 703–708. https://doi.org/10.2307/1913610.
Pan, Jiapu and Willis J. Tompkins. “A Real-Time QRS Detection Algorithm.” IEEE Transactions on Biomedical Engineering, vol. BME-32, no. 3, 1985, pp. 230-236. https://doi.org/10.1109/tbme.1985.325532.
White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, PMLR, vol. 70, 2017, pp. 1321–1330. https://proceedings.mlr.press/v70/guo17a.html.
Hu, Edward J., et al. “LoRA: Low-Rank Adaptation of Large Language Models.” International Conference on Learning Representations, 2022. https://openreview.net/forum?id=nZeVKeeFYf9.
Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017. https://arxiv.org/abs/1705.08500.