Reinforcement Learning-Based Adaptive Noise Suppression for Real-Time Biomedical Signal Processing
Keywords:
reinforcement learning, adaptive noise suppression, biomedical signal processing, real-time systems, clinical AI governanceAbstract
Reinforcement learning offers a principled approach for adapting noise suppression policies in real-time biomedical signal processing. This paper presents a system-level analysis of adaptive noise suppression architectures that integrate reinforcement learning with conventional signal conditioning, physiological monitoring, and safety supervision. We examine the structural trade-offs between rapid adaptation and stability, between edge autonomy and centralized oversight, and between learning flexibility and clinical interpretability. The discussion emphasizes the design of acquisition pipelines, state representation, action constraints, reward shaping, and runtime safety monitors. Rather than proposing a single algorithm, the paper develops a governance-oriented framework for designing and deploying learning-based noise suppression systems in clinical and ambulatory settings. The analysis covers robustness to sensor degradation, motion artifact, electrode impedance changes, and population heterogeneity. We further address fairness, accountability, sustainability, and regulatory policy implications. The treatment is intentionally at the system level, connecting control-theoretic filtering, deep learning, clinical deployment, and infrastructure governance. It is argued that reinforcement learning can improve noise suppression only when embedded within a broader socio-technical architecture that includes transparent fallback mechanisms, continuous evaluation, and clear regulatory pathways. This perspective provides a foundation for subsequent empirical and clinical validation of real-time learning-based biomedical signal enhancement.
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