Markov Chain Modelling for Heart Rate Variability Analysis: Bridging Artificial Intelligence and Physiological Data

Heart rate variability (HRV) is a vital measure that captures the variations in beat-to-beat intervals. It is a significant indicator of autonomic nervous system activity and has been linked to various health conditions. Analyzing HRV involves processing complex physiological data with inherent uncertainties. Artificial Intelligence (AI) refers to the development of intelligent machines capable of mimicking human intelligence and learning. Markov Chain theory provides a powerful framework for studying and mathematically modelling situations with random variables. In this paper, we explore the application of Markov Chain concepts to HRV analysis, treating it as an AI problem. We represent beat-to-beat intervals as a sequence of random variables, forming Markov Chain states where the current state depends on the immediate previous state. We derive the state-to-state probability transition matrix and compute the stationary distribution probabilities. Using these probabilities, we calculate the Shannon entropy measure to gain insights into heart rate variability. Furthermore, we present the utilization of real-life experimental data to illustrate the effectiveness of the proposed method. The experimental results demonstrate the promising potential of the integration of Markov Chain and AI in analyzing HRV by achieving the highest accuracy of 95%. This research a novel perspective on understanding the underlying dynamics of HRV and its implications for human health.