Hybrid Deep Reinforcement Learning for Multimodal Biomedical Signal Fusion

Bhanu Prakash Reddy Rella; Rajesh Sura; Rahul Kumar Konduru; Bosko Nikolic; Milos Janjic; Nebojsa Bacanin

doi:10.3791/69929

Research Article

Hybrid Deep Reinforcement Learning for Multimodal Biomedical Signal Fusion

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DOI:

10.3791/69929

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April 10th, 2026

Bhanu Prakash Reddy Rella¹ , Rajesh Sura² , Rahul Kumar Konduru³ , Bosko Nikolic⁴ , Milos Janjic⁵ , Nebojsa Bacanin⁶^,⁷

¹Ageno School of Business, Golden Gate University, ²Department of Electrical and Electronics Engineering, Anna University, ³Lead Data Engineer, FedEx Dataworks, ⁴School of Electrical Engineering, University of Belgrade, ⁵Innovation Centre, School of Electrical Engineering, University of Belgrade, ⁶Faculty of Informatics and Computing, Singidunum University, ⁷Department of Mathematics, Saveetha School of Engineering, SIMATS

Summary

This research proposes a hybrid deep reinforcement learning (HDRL) framework that integrates deep neural networks (DNNs) for feature extraction with reinforcement learning (RL) for dynamic multimodal signal fusion. Evaluated on biomedical datasets, it excels in accuracy, robustness, and rare-event detection, providing major applications in personalized medicine and disease classification.

Abstract

Improving decision-making and diagnostic accuracy in health care requires combining multimodal biomedical signals such as electrocardiograms (ECG), electroencephalograms (EEG), and medical imaging. Traditional signal fusion techniques face significant challenges due to signal variability and inherent complexity. We propose a Hybrid Deep Reinforcement Learning (HDRL) model that employs Deep Neural Networks (DNNs) for feature extraction and Reinforcement Learning (RL) for dynamic fusion optimization. This hybrid model utilizes real-time feedback to optimize fusion policy learning and adapt to varying data characteristics. To evaluate its performance, the proposed model was compared with conventional fusion techniques, such as Deep Neural Network-based methods and Principal Component Analysis (PCA). The hybrid deep reinforcement learning model was tested on several real-world biomedical datasets. The proposed model is applicable to personalised medicine and disease classification, as experimental evidence demonstrates improved performance in terms of robustness under noisy conditions, classification accuracy, and rare-event detection. The proposed methodology, in addition to addressing concerns with traditional signal fusion techniques, offers promising solutions that advance multimodal biomedical signal processing. The model uses a DNN for feature mining and RL agents (DQN, PPO) for policy optimization.

Introduction

Modern healthcare increasingly relies on integrating diverse biomedical data sources to improve diagnostic accuracy and clinical decision-making. Physiological signals such as electrocardiograms (ECG), electroencephalograms (EEG), and medical imaging modalities including magnetic resonance imaging (MRI) and computed tomography (CT) provide complementary physiological information that supports disease diagnosis and personalized treatment. However, biomedical signals are inherently complex and often contain noise, missing values, and high-dimensional structures, making their analysis and interpretation challenging. Multimodal biomedical signal fusion, which integrates i....