AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts.

purposeEffective patient monitoring is crucial for timely healthcare interventions and improved outcomes, especially in managing conditions influenced by stress and depression, which can manifest through physiological changes. Traditional monitoring systems often struggle with the complexity and dynamic nature of such conditions, leading to delays in identifying critical scenarios. This study proposes a novel multi-agent deep reinforcement learning (DRL) framework to address these challenges by monitoring vital signs and providing real-time decision-making capabilities.

methodsOur framework deploys multiple learning agents, each dedicated to monitoring specific physiological features such as heart rate, respiration, and temperature. These agents interact with a generic healthcare monitoring environment, learn patients' behavior patterns, and estimate the level of emergency to alert Medical Emergency Teams (METs) accordingly. The study evaluates the proposed system using two real-world datasets-PPG-DaLiA and WESAD-designed to capture physiological and stress-related data. The performance is compared with baseline models, including Q-Learning, PPO, Actor-Critic, Double DQN, and DDPG, as well as existing monitoring frameworks like WISEML and CA-MAQL. Hyperparameter optimization is also performed to fine-tune learning rates and discount factors.

resultsExperimental results demonstrate that the proposed multi-agent DRL framework outperforms baseline models in accurately monitoring patients' vital signs under stress and varying conditions. The optimized agents adapt effectively to dynamic environments, ensuring timely detection of critical health deviations. Comparative evaluations reveal superior performance in metrics related to decision-making accuracy and response efficiency, highlighting the robustness of the framework.

conclusionsThe proposed AI-driven monitoring system offers significant advancements over traditional methods by handling complex and uncertain environments, adapting to varying patient conditions influenced by stress and depression, and making autonomous, real-time decisions. While the framework demonstrates high accuracy and adaptability, challenges related to data scale and future vital sign prediction remain. Future research will focus on extending predictive capabilities to further enhance proactive healthcare interventions.