The multikinetic fusion feature of PPG was combined with MCNN_vision_transformer for diabetes detection.

Abstract

Background: Diabetes is a chronic condition that significantly impacts the cardiovascular system and various other organs. Photoplethysmogram (PPG) signals have been shown to correlate with variations in vascular blood flow and the presence of atherosclerosis. To effectively explore the complex nonlinear relationship between PPG signals and diabetes, we propose an automatic detection model based on the fusion of PPG features.

Methods: The proposed model consists of two main components: 1. Dynamic Fusion Feature Extraction: Short PPG signal window segments are processed using the SGR spatial encoding algorithm to extract dynamic fusion features. 2. Feature Representation Learning: Multi-scale convolutional layers (MCNN) are employed to learn feature representations, while the Vision Transformer (ViT) model is utilized to capture global contextual semantic features.

Results: The model was trained and validated on a self-collected medical dataset. The experimental results demonstrate that the classification model, which integrates short time window information, significantly improves detection performance. Specifically, the multi-period sequence input model achieves an accuracy of 91.11%, with a Receiver Operating Characteristic (ROC) curve area of 0.9341, indicating strong diagnostic capability.

Conclusion: This study is a retrospective case-control study that collected clinical data from three groups of people: those with normal glucose levels, those with poorly controlled diabetes, and those with well-controlled diabetes. The study aims to utilize deep learning algorithms for the early prevention and screening of diabetes.