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[2510.17406] Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals
Summary
This paper presents S4ECG, a novel deep learning architecture that enhances arrhythmia classification by analyzing multiple ECG windows, improving accuracy and reducing false positives.
Why It Matters
Accurate arrhythmia classification is crucial for timely medical intervention. This study addresses the limitations of traditional methods by leveraging long-range dependencies in ECG signals, potentially improving patient outcomes and diagnostic practices in cardiology.
Key Takeaways
- S4ECG architecture captures long-range temporal dependencies in ECG signals.
- Multi-window analysis significantly improves arrhythmia classification accuracy.
- Optimal diagnostic windows for ECG monitoring are identified as 10-20 minutes.
- The approach reduces false positive rates for atrial fibrillation detection.
- Cross-dataset robustness is enhanced, making the model more reliable across different datasets.
Computer Science > Machine Learning arXiv:2510.17406 (cs) [Submitted on 20 Oct 2025 (v1), last revised 12 Feb 2026 (this version, v3)] Title:Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals Authors:Tiezhi Wang, Wilhelm Haverkamp, Nils Strodthoff View a PDF of the paper titled Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals, by Tiezhi Wang and 1 other authors View PDF HTML (experimental) Abstract:Objective. Arrhythmia classification from electrocardiograms (ECGs) suffers from high false positive rates and limited cross-dataset generalization, particularly for atrial fibrillation (AF) detection where specificity ranges from 0.72 to 0.98 using conventional 30-s analysis windows. While most deep learning approaches analyze isolated 30-s ECG windows, many arrhythmias, including AF and atrial flutter, exhibit diagnostic features that emerge over extended time scales. Approach. We introduce S4ECG, a deep learning architecture based on structured state-space models (S4), designed to capture long-range temporal dependencies by jointly analyzing multiple consecutive ECG windows spanning up to 20 min. We evaluate S4ECG on four publicly available databases for multi-class arrhythmia classification and perform systematic cross-dataset evaluations to assess out-of-distribution robustness. Results. Multi-window analys...
