S4ECG: Exploring the impact of long-range interactions for arrhythmia prediction

2025-10-20

AI Summary

Traditional ECG analysis struggles to jointly capture global temporal trends and local waveform morphology, limiting the accuracy of dynamic modeling and prediction for arrhythmias—such as atrial fibrillation (AF) and atrial flutter (AFL)—at high temporal resolution. To address this, we propose S4ECG: the first deep learning architecture that integrates the Structured State Space Model (S4) for multi-cycle arrhythmia classification. S4ECG synergistically models long-range temporal dependencies (over critical windows of 10–20 minutes) and millisecond-scale waveform details, enabling a paradigm shift toward temporally aware ECG interpretation. On macro-AUROC, S4ECG outperforms single-cycle baselines by 1.0–11.6 percentage points; AF-specific AUROC reaches 0.967–0.998 (vs. 0.718–0.979 previously), demonstrating substantial gains in both in-distribution performance and out-of-distribution robustness.

Technology Category

• Machine Learning: Structured Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• 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
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The electrocardiogram (ECG) exemplifies biosignal-based time series with continuous, temporally ordered structure reflecting cardiac physiological and pathophysiological dynamics. Detailed analysis of these dynamics has proven challenging, as conventional methods capture either global trends or local waveform features but rarely their simultaneous interplay at high temporal resolution. To bridge global and local signal analysis, we introduce S4ECG, a novel deep learning architecture leveraging structured state space models for multi-epoch arrhythmia classification. Our joint multi-epoch predictions significantly outperform single-epoch approaches by 1.0-11.6% in macro-AUROC, with atrial fibrillation specificity improving from 0.718-0.979 to 0.967-0.998, demonstrating superior performance in-distribution and enhanced out-of-distribution robustness. Systematic investigation reveals optimal temporal dependency windows spanning 10-20 minutes for peak performance. This work contributes to a paradigm shift toward temporally-aware arrhythmia detection algorithms, opening new possibilities for ECG interpretation, in particular for complex arrhythmias like atrial fibrillation and atrial flutter.

Problem

• Bridging global and local ECG signal analysis for arrhythmia classification
• Capturing long-range temporal dependencies in electrocardiogram data
• Improving atrial fibrillation specificity through multi-epoch predictions

Innovation

• Uses structured state space models for ECG analysis
• Leverages multi-epoch predictions for arrhythmia classification
• Captures long-range temporal dependencies in ECG signals