Multimodal Deep Learning for Non-Linear Signal Interpretation in Psychiatric Diagnostics

Yixuan Pan^*

• Shandong Jiaotong University, Jinan, China

The increasing complexity of high-dimensional sensory data in psychiatric diagnostics necessitates advanced methodologies capable of modeling nonlinear and heterogeneous signals. Conventional approaches often treat modalities independently, overlooking critical inter-modal relationships and semantic consistencies that are pivotal for accurate and comprehensive mental health assessments. Addressing this challenge, we propose a multimodal deep learning framework tailored for intelligent nonlinear signal interpretation. Central to this framework is the Fusion-Aware Relational Encoder (FARE), an innovative architecture designed to capture high-order interactions among modalities while maintaining their unique characteristics through relational attention mechanisms and adaptive fusion techniques. Additionally, the framework incorporates a Modality-Aligned Optimization Strategy (MAOS) to ensure balanced learning by integrating uncertainty regularization, cross-modal contrastive alignment, and a curriculum-based training methodology. This synergistic combination significantly mitigates issues such as gradient conflicts and modality imbalance, enhancing semantic coherence and model robustness. Extensive empirical evaluations conducted on diverse multimodal psychiatric datasets demonstrate the framework's superior performance in classification accuracy and interpretability, highlighting its potential to advance the field of nonlinear signal processing for intelligent psychiatric diagnostics.