Research on Multimodal Biomedical Signal Recognition

This study proposes a multimodal biomedical signal recognition algorithm that aims to fuse image data and biosignal data to improve the accuracy and real-time performance of medical data analysis. The algorithm employs a deep learning model to independently recognize the two data types and introduces a cross-attention mechanism to assess the correlation between image and biosignal features. The mechanism dynamically adjusts the attention weights so as to effectively suppress irrelevant features and promote the fusion of image and signal features. Specifically, the biosignal features are extracted by the temporal Kolmogorov-Arnold network (TKAN), while the image data are processed by the YOLOv11 network. These two feature vectors are aligned and fused through the cross-attention mechanism, and finally the fused feature vectors are generated and classified through the fully connected layer to obtain the final diagnosis results. This paper also explores pre-processing techniques for image and signal data, including methods for denoising, signal enhancement, data normalization and image alignment. These preprocessing steps effectively improve the quality of the raw data and provide clearer and more accurate inputs for subsequent deep learning models. Through experimental verification, the proposed algorithm outperforms traditional methods in a simulated environment, especially in real-time data analysis and multimodal fusion accuracy.