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

Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Hong-Ning Dai, Feng Zhao, Jianming Yong

Abstract: Effective 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.

External IDs:dblp:journals/braininf/ShaikTLXDZY25