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.