An improved ICEEMDAN–depth hybrid network model integrating multimodal data for the screening of diabetic peripheral neuropathy

Early non-invasive approaches for detecting diabetic peripheral neuropathy (DPN) are crucial to preventing its severe complications. However, these approaches have been limited by insufficient dynamic feature capture, low model efficiency, and poor portability. To improve the non-invasive detection capability for DPN, a novel combined method based on the fusion of PPG and ECG signals is proposed. Firstly, an adaptive denoising method integrating ICEEMDAN-based signal decomposition, wavelet thresholding, and particle swarm optimization is adopted to improve signal quality. Secondly, a combined encoding framework, integrating spatial position encoding, Grampian angular field, and recurrence plot, is employed to transform one-dimensional time-series signal segments into RGB color maps. Finally, an enhanced lightweight network named Afsharid, incorporating multi-branch depth wise convolution and a spatial hybrid self-attention mechanism, is designed to generate fused RGB representations. On the multi-cycle dataset, the proposed model achieved an accuracy of 93.89%, a sensitivity of 93.21%, and a precision of 94.52%. Compared with the best-performing baseline model EfficientNetV2, the accuracy was improved by 6.52%. The results show the feasibility and potential of the combined method as a new solution for early detection and daily monitoring of DPN.