AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts
Abstract Purpose 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 complexi...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Brain Informatics |
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| Online Access: | https://doi.org/10.1186/s40708-025-00262-1 |
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| author | Thanveer Shaik Xiaohui Tao Lin Li Haoran Xie Hong-Ning Dai Feng Zhao Jianming Yong |
| author_facet | Thanveer Shaik Xiaohui Tao Lin Li Haoran Xie Hong-Ning Dai Feng Zhao Jianming Yong |
| author_sort | Thanveer Shaik |
| collection | DOAJ |
| description | Abstract Purpose 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. Methods Our 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. Results Experimental 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. Conclusions The 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. |
| format | Article |
| id | doaj-art-1f21d37751e1407dbd57f553d202b433 |
| institution | OA Journals |
| issn | 2198-4018 2198-4026 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj-art-1f21d37751e1407dbd57f553d202b4332025-08-20T02:06:36ZengSpringerOpenBrain Informatics2198-40182198-40262025-06-0112111810.1186/s40708-025-00262-1AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contextsThanveer Shaik0Xiaohui Tao1Lin Li2Haoran Xie3Hong-Ning Dai4Feng Zhao5Jianming Yong6School of Mathematics, Physics & Computing, University of Southern QueenslandSchool of Mathematics, Physics & Computing, University of Southern QueenslandSchool of Computer and Artificial Intelligence, Wuhan University of TechnologyDivision of Artificial Intelligence, School of Data Science, Lingnan UniversityDepartment of Computer Science, Hong Kong Baptist UniversityHuazhong University of Science and TechnologySchool of Business, University of Southern QueenslandAbstract Purpose 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. Methods Our 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. Results Experimental 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. Conclusions The 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.https://doi.org/10.1186/s40708-025-00262-1Behavior patternsDecision makingPatient monitoringReinforcement learningVital signs |
| spellingShingle | Thanveer Shaik Xiaohui Tao Lin Li Haoran Xie Hong-Ning Dai Feng Zhao Jianming Yong AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts Brain Informatics Behavior patterns Decision making Patient monitoring Reinforcement learning Vital signs |
| title | AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts |
| title_full | AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts |
| title_fullStr | AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts |
| title_full_unstemmed | AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts |
| title_short | AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts |
| title_sort | ai driven multi agent reinforcement learning framework for real time monitoring of physiological signals in stress and depression contexts |
| topic | Behavior patterns Decision making Patient monitoring Reinforcement learning Vital signs |
| url | https://doi.org/10.1186/s40708-025-00262-1 |
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