An AI-Assisted Research Study Demonstrating the Importance of Shannon Entropy in Detecting Atrial Fibrillation through Heart Rate Time Series ATRIAL FIBRILLATION AND SHANNON ENTROPY
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Abstract
Prompt detection of Atrial Fibrillation is crucial to avert the serious complications linked to this arrhythmia. The diagnosis obtained from ECG-Holter Monitoring is unreliable unless the arrhythmia occurs during the course of these examinations. The paper presents a novel and robust methodology for the prediction and diagnosis of Atrial Fibrillation, employing Heart Rate Variability analysis of a patient, grounded in the most advanced techniques of the statistical mechanics of complex disordered systems, and suitable for integration into clinical practice. The methodology has also employed Artificial Intelligence (following an adequate period of Machine Learning) to verify the results via a secondary, independent process. The research is an observational study involving several thousand individuals who underwent experimental heart rate monitoring and subsequent variability analyses. Among the numerous markers evaluated in this analysis, four of them demonstrate the ability to detect and diagnose fibrillation with high sensitivity but limited specificity, and only if AFIB occurs during the monitoring period. Notably, one indicator, Shannon Entropy, exhibits exceptional performance by effectively detecting Atrial Fibrillation with both high sensitivity and specificity, and even if episodes occurred in the recent or distant past, demonstrating a significant "memory effect”. This fact provides clinicians with an innovative approach for detecting and or predicting this important arrhythmia, even in the absence of ECG analysis, by solely monitoring the patient's heart rate over a 24-hour period. This approach substantially enhances the detection of AFIB episodes and facilitates the development of preventive measures and prophylactic therapies to mitigate the adverse effects of the arrhythmia.
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References
2. Barra O.A. et al.: Analysis of temporal correlation in heart rate variability through maximum entropy principle in a minimal pairwise glassy model"; Scientific Reports 10, 15353 (2020); DOI: https://doi.org/10.1038/s41598-020-72183-4
3. Hart, R.G.&Halperin, J.L. Atrial fibrillation and stroke: concepts and controversies. Stroke 32 (3), 803-808 (2001)
4. Lip, G.Y.& Lim, H.S. Atrial fibrillation and stroke prevention. The Lancet Neurology 6 ( 11), 981-993 (2007)
5. Binici, Z., Intziakilis, T., Nielsen, O.W., Kober, L., Sajadieh, A. Excessive supraventricular ectopic activity and increased risk of atrial fibrillation and stroke. Circulation 121 (17), 1904 (2010)
6. Albers, G.W. Atrial fibrillation and stroke: three new studies, three remaining questions. Archives of Internal Medicine 154 (13), 1443-1448 (1994)
7. Ezekowitz, M.D. et al. Warfarin in the prevention of stroke associated with non-rheumatic atrial fibrillation. New England Journal of Medicine 327 (20), 1406-1412(1992)
8. Knecht, S.et al.: Atrial fibrillation in stroke-free patients is associated with memory impairment and hippocampal atrophy. European Heart Journal 29 (17), 2125-2132 (2008)
9. 2024 ESC Guidelines for the management of atrial fibrillation, Clinical Practice Guidelines (2024)
10. Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology: Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 93 (5), 1043–1065 (1996)
11. Olesen JB, Lip GY, Hansen ML, et al. Validation of risk stratification schemes for predicting stroke and thromboembolism in patients with atrial fibrillation: nationwide cohort study. BMJ. 342 (2011) , doi: 10.1136/bmj.d124. PubMed ID: 21282258
12. D'Agostino, R.B., Vasan R.S. et al. (2008). "General cardiovascular risk profile for use in primary care: the Framingham Heart Study”., Circulation, 117(6), 743-53 (2008)
13. Barra, O.A. & Moretti, L. The "Life Potential": a new complex algorithm to assess "Heart Rate Variability" from Holter records for cognitive and diagnostic aims. Preliminary experimental results showing its dependence on age, gender and health conditions. Preprint at https://arxiv.org/abs/1310.7230 (2013)
14. Shannon, C.E.& Weaver, W. The Mathematical Theory of Communication, Book Edited by University of Illinois Press, Urbana (1949, republished on 1998)
15. Jaynes, E.T. Information Theory and Statistical Mechanics. Phys. Rev. 106, 620, DOI: https://doi.org/10.1103/PhysRev.106.620 (1957)
16. Zurek, W.H. Complexity entropy and the Physics of information. The Proceedings Of The Workshop On Complexity, Entropy, And The Physics Of Information Held May-June, 1989 In Santa Fe, New Mexico, Volume VIII , Santa Fe Institute Studies in the Sciences of Complexity, CRC Press (2018)
17. Cerf,N.J. &Adami, C. Negative Entropy and Information in Quantum Mechanics. Phys. Rev. Lett. 79, 5194 (1997)
18. Gappmair, W. Claude E. Shannon: the 50th anniversary of information theory.IEEE Communications Magazine 37, 4 , 102-105 DOI: https://doi.org/10.1109/35.755458(1999)
19. Goedeken, E.A. A Mind at Play: How Claude Shannon Invented the Information Age. Information & Culture: A Journal of History, University of Texas Press 53, 3/4 , 377-378 (2018)
20. Steeb, W.H. A Handbook of Terms used in Chaos and Quantum Chaos, BI-Wissenschaftsverlag, Mannheim, (1991).
21. Zaslavsy, G.M. Chaos in Dynamic Systems, Harwood, Chur (1985)
22. Mandelbrot B.B., The Fractal Geometry of Nature, Freeman, San Francisco (1982)
23. Yamamoto, Y,. & Hughson, R.L. Coarse-graining spectral analysis: new method for studying heart rate variability. J Appl Physiol. 71, 1143-1150 (1991)
24. Babloyantz, A &Destexhe, A. Is the normal heart a periodic oscillator? BiolCybern. 58, 203-211 (1988)
25. Saul, JP, Albrecht, P, Berger, R.D., Cohen, R.J. Analysis of long-term heart rate variability: methods, 1/f scaling and implications. In: Computers in Cardiology 1987. IEEE Computer Society Press .Washington, DC; 419-422 (1988)
26. Brennan, M,Palaniswami, M, Kamen, P.Do existing measures of Poincaré plot geometry reflect non-linear features of heart rate variability? Proc. IEEE Transactions on Biomedical Engineering, 48, 1342-1347 (2001)
27. Kanters, J.K., Holstein-Rathlou, N.H., Agner, E. "Lack of evidence for low-dimensional chaos in heart rate variability". Journal of Cardiovascular Electrophysiology 5, 7, 591–601 (1994)
28. Storella, R.J. et al."Approximate entropy and point correlation dimension of heart rate variability in healthy subjects". Integrative Physiological & Behavioral Science 33, 4, 315–20 (1994)
29. Carrasco S., M.J. Cait´an, R. Gonz´alez, and O. Y´anez. Correlation among Poincar´e plot indexes and time and frequency domain measures of heart rate variability. J MedEngTechnol, 25, 240–248 (2001)
30. Kobayashi ,M, Musha,T. 1/f fluctuation of heart beat period. IEEE Trans Biomed Eng. 29, 456- 457 (1982)
31. Guzzetti, S. et al. Non-linear dynamics and chaotic indices in heart rate variability of normal subjects and heart-transplanted patients. Cardiovascular Research 31, 441–446 (1996)
32. Lake, D.E., Richman, J.S., Griffin, M.P., Moorman, J.R., Sample entropy analysis of neonatal heart rate variability. Am.Jour.of Phys. 283, R789–R797 (2002)
33. Penzel T., Kantelhardt J.W., Grote L., Peter J-H., and Bunde A., Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Trans Biomed Eng. 50, 10, 1143–1151 (2003)
34. Richman J.A. and Moorman J.R., Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol. 278, H2039–H2049 (2000)
35. Webber, C.L. Jr. &Zbilut, J.P., Dynamical assessment of physiological systems and states using recurrence plot strategies. J ApplPhysiol, 76,965–973 (1994)
36. Bracewell, R.N. The Fourier transform and its applications. Book 3rd.Ed. Boston: McGraw Hill (2000) ISBN: 0-07-303938-1
37. Vandeput, S., Verheyden, B., Aubert, A.E., Van Huffel S.M. Non-Linear Heart Rate Dynamics: Circadian Profile and Influence of Age and Gender. Med. Eng.and Phys. 34, 108-117 (2012)
38. Goldberger, A.L. et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101, 215-220 (2000)
39. Aliot, E., Haissaguerre,M., Jackman, W. Catheter Ablation of Atrial Fibrillation. Book Wiley-Blackwell Ed. (2008) ISBN: 978-1-4051-6349-1
40. Haissaguerre, M. and Zipes,D. Thoracic Vein Arrhythmias: Mechanisms and Treatment. Book Blackwell Futura Ed. (2004) ISBN: 978-1-4051-1888-0