Cross-Patch Guided Reconstruction With Graph Contrastive Learning for Smartphone-Based Early Parkinson’s Disease Detection

Abstract

Self-supervised learning for time-series data has broad application potential in smartphone-based early disease detection. However, time-series data often exhibit complex dynamic patterns and spatiotemporal correlations. These characteristics make it difficult to capture discriminative features and reconstruct local features. Therefore, this article proposes a cross-patch guided reconstruction with a graph contrastive learning framework (CGR-GCL) for smartphone-based early disease detection. CGR-GCL combines the advantages of mask reconstruction to capture complex dynamic patterns and contrastive learning to extract discriminative features. For the mask reconstruction part, we design an RGB-guided multiscale masking module. Time-series data are first converted into RGB images. These images are then used to generate attention maps and multiscale masks. The masks apply larger occlusions to important regions of the time series and smaller ones to less critical regions. This structural prior and semantic guidance help the model focus on key information and filter out redundant information. In parallel, we develop a dual-granularity contrastive module based on graph neural networks (GNNs). This module is able to capture spatial–temporal dependencies and enhance local consistency modeling. Compared with six baseline algorithms, CGR-GCL generally outperforms them (e.g., achieving +6.00 % accuracy and +6.20  % macro $F1$ -score (M $F1$ -score) on the mPowerTapping dataset with 10 % labels). Meanwhile, a series of ablation results demonstrate the positive contributions of different modules to the performance improvement and the rationality of the overall framework. Our code is publicly available at https://github.com/heyiyia/CGR-GCL.git