Keywords
Cardiac signal
Explainable AI
Early-warning prediction
How to Cite
Abstract
Cardiac fibrillation is widely regarded as a transition from organized electrical activity to sustained spatiotemporal chaos, yet early indicators of this transition remain difficult to extract from sparse clinical measurements. We present a simulation-based, interpretable early-warning framework for predicting rotor breakup. A two-variable excitable-medium model generates a single pinned rotor that destabilizes into defect-mediated turbulence under gradually increasing tissue excitability. Phase-singularity counting provides the ground-truth breakup onset. Using signals from a sparse electrode grid, physiologically meaningful features are extracted over sliding windows during the organized phase and used to train a gradient-boosted decision-tree classifier, evaluated with simulation-grouped data splits to prevent leakage. The model predicts impending breakup with an area under the receiver-operating-characteristic curve of 0.997 and a median warning lead time of approximately six time units.Explainable additive attributions identify the dominant precursors as the spatial dispersion of activation timing, beat-to-beat cycle-length variability, and the dispersion of signal complexity across electrodes, all of which are consistent with restitution- and dispersion-driven destabilization theory. The framework yields transparent, mechanism-aligned warnings from device-measurable observables and points toward interpretable early-warning modules for implantable and wearable cardiac monitors.
References
Aliev, R. R. and A. V. Panfilov, 1996. A simple two-variable model of cardiac excitation. Chaos, Solitons & Fractals, 7, 293–301.
Attia, Z. I., P. A. Noseworthy, F. Lopez-Jimenez, S. J. Asirvatham, A. J. Deshmukh, et al., 2019. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm. The Lancet, 394, 861–867.
Bär, M. and M. Eiswirth, 1993. Turbulence due to spiral breakup in a continuous excitable medium. Physical Review E, 48, R1635–R1637.
Bär, M. and M. Or-Guil, 1999. Alternative scenarios of spiral breakup in a reaction-diffusion model with excitable and oscillatory dynamics. Physical Review Letters, 82, 1160–1163.
Bray, M.-A. and J. P. Wikswo, 2002. Considerations in phase plane analysis for nonstationary reentrant cardiac behavior. Physical Review E, 65, 051902.
Chen, T. and C. Guestrin, 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794.
Cherry, E. M. and F. H. Fenton, 2008. Visualization of spiral and scroll waves in simulated and experimental cardiac tissue. New Journal of Physics, 10, 125016.
Clayton, R. H., O. Bernus, E. M. Cherry, H. Dierckx, F. H. Fenton, et al., 2011. Models of cardiac tissue electrophysiology: Progress, challenges and open questions. Progress in Biophysics and Molecular Biology, 104, 22–48.
Cross, M. C. and P. C. Hohenberg, 1993. Pattern formation outside of equilibrium. Reviews of Modern Physics, 65, 851–1112.
Dakos, V., S. R. Carpenter, W. A. Brock, A. M. Ellison, V. Guttal, et al., 2012. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE, 7, e41010.
Davidenko, J. M., A. V. Pertsov, R. Salomonsz, W. Baxter, and J. Jalife, 1992. Stationary and drifting spiral waves of excitation in isolated cardiac muscle. Nature, 355, 349–351.
Fenton, F. H., E. M. Cherry, H. M. Hastings, and S. J. Evans, 2002. Multiple mechanisms of spiral wave breakup in a model of cardiac electrical activity. Chaos, 12, 852–892.
FitzHugh, R., 1961. Impulses and physiological states in theoretical models of nerve membrane. Biophysical Journal, 1, 445–466.
Gray, R. A., A. M. Pertsov, and J. Jalife, 1998. Spatial and temporal organization during cardiac fibrillation. Nature, 392, 75–78.
Hannun, A. Y., P. Rajpurkar, M. Haghpanahi, G. H. Tison, C. Bourn, et al., 2019. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25, 65–69.
Iyer, A. N. and R. A. Gray, 2001. An experimentalist's approach to accurate localization of phase singularities during reentry. Annals of Biomedical Engineering, 29, 47–59.
Karma, A., 1994. Electrical alternans and spiral wave breakup in cardiac tissue. Chaos, 4, 461–472.
Lundberg, S. M., G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, et al., 2020. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.
Lundberg, S. M. and S.-I. Lee, 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, Vol. 30, pp. 4765–4774.
Mikhailov, A. S. and K. Showalter, 2006. Control of waves, patterns and turbulence in chemical systems. Physics Reports, 425, 79–194.
Narayan, S. M., D. E. Krummen, K. Shivkumar, P. Clopton, W.-J. Rappel, et al., 2012. Treatment of atrial fibrillation by the ablation of localized sources. Journal of the American College of Cardiology, 60, 628–636.
Nattel, S., 2002. New ideas about atrial fibrillation 50 years on. Nature, 415, 219–226.
Pandit, S. V. and J. Jalife, 2013. Rotors and the dynamics of cardiac fibrillation. Circulation Research, 112, 849–862.
Panfilov, A. V., 1998. Spiral breakup as a model of ventricular fibrillation. Chaos, 8, 57–64.
Qu, Z., J. N. Weiss, and A. Garfinkel, 1999. Cardiac electrical restitution properties and stability of reentrant spiral waves: A simulation study. American Journal of Physiology-Heart and Circulatory Physiology, 276, H269–H283.
Rudin, C., 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215.
Scheffer, M., J. Bascompte, W. A. Brock, V. Brovkin, S. R. Carpenter, et al., 2009. Early-warning signals for critical transitions. Nature, 461, 53–59.
Topol, E. J., 2019. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25, 44–56.
Trayanova, N. A., 2011. Whole-heart modeling: Applications to cardiac electrophysiology and electromechanics. Circulation Research, 108, 113–128.
Weiss, J. N., Z. Qu, P.-S. Chen, S.-F. Lin, H. S. Karagueuzian, et al., 2005. The dynamics of cardiac fibrillation. Circulation, 112, 1232–1240.
Winfree, A. T., 1989. Electrical instability in cardiac muscle: Phase singularities and rotors. Journal of Theoretical Biology, 138, 353–405.
Witkowski, F. X., L. J. Leon, P. A. Penkoske, W. R. Giles, M. L. Spano, et al., 1998. Spatiotemporal evolution of ventricular fibrillation. Nature, 392, 78–82.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.