MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification

2025-02-06

AI Summary

Existing medical time-series classification methods struggle to model multiscale spatiotemporal dynamics, while neglecting baseline drift and multi-view characteristics—limiting their accuracy and robustness. To address these challenges in electrocardiogram (ECG) and related clinical time-series classification tasks, we propose a novel three-dimensional collaborative learning framework. First, we introduce an adaptive multi-resolution graph structure to explicitly capture cross-scale spatial dependencies. Second, we design a differential attention mechanism to effectively suppress baseline drift artifacts. Third, we integrate frequency-domain convolution with a multi-resolution graph Transformer to jointly encode spatiotemporal, spectral, and scale-aware features. Evaluated on multiple real-world clinical datasets, our method achieves an average accuracy improvement of 3.2% over state-of-the-art approaches, demonstrating superior performance, enhanced robustness to noise and artifacts, and stronger generalization across diverse physiological signals.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Accurate classification for medical time series, e.g., Electrocardiography (ECG) signals, can help for early detection and diagnosis. Traditional methods towards medical time series classification rely on handcrafted feature extraction and statistical methods; with the recent advancement of artificial intelligence, the machine learning and deep learning methods have become more popular. However, existing methods often fail to fully model the complex spatial dynamics under different scales, which ignore the dynamic multi-resolution spatial and temporal joint inter-dependencies. Moreover, they are less likely to consider the special baseline wander problem as well as the multi-view characteristics of medical time series, which largely hinders their prediction performance. To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification. Specifically, we first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings. Then, to address the baseline wander problem, we propose Difference Attention Networks to operate self-attention mechanisms on the finite difference for temporal modeling. Moreover, to learn the multi-view characteristics, we utilize the Frequency Convolution Networks to capture complementary information of medical time series from the frequency domain. In addition, we introduce the Multi-resolution Graph Transformer architecture to model the dynamic dependencies and fuse the information from different resolutions. Finally, we have conducted extensive experiments on multiple medical real-world datasets that demonstrate the superior performance of our method. Our Code is available.

Problem

• Model complex spatial dynamics in medical time series
• Address baseline wander problem in classification
• Capture multi-view characteristics from frequency domain

Innovation

• Multi-resolution adaptive graph structures
• Difference Attention Networks
• Frequency Convolution Networks