Hybrid Deep Reinforcement Learning for Multimodal Biomedical Signal Fusion

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.