Parallel-Learning of Invariant and Tempo-variant Attributes of Single-Lead Cardiac Signals: PLITA

2025-02-28

AI Summary

Existing self-supervised learning (SSL) methods for single-lead ECG modeling primarily capture static, invariant features while neglecting time-varying physiological dynamics that reflect a patient’s evolving physiological state. To address this limitation, we propose PLITA—the first SSL framework that explicitly disentangles and jointly optimizes invariant and time-variant representations. PLITA employs a dual-branch parallel encoder architecture: one branch models subject-intrinsic, stable characteristics, while the other captures state-evolving, tempo-variant patterns. A novel temporal proximity constraint is introduced to explicitly guide the learning of discriminative time-variant features. Evaluated on wearable ECG analysis tasks, PLITA significantly outperforms state-of-the-art SSL baselines. Notably, on physiology-dependent dynamic tasks—including emotion recognition and fatigue detection—PLITA achieves up to a 12.7% improvement in AUC, demonstrating its effectiveness in leveraging temporal physiological dynamics for robust representation learning.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digital health. Unsupervised learning frameworks such as Self-Supervised Learning (SSL) are essential to map these single-lead electrocardiogram (ECG) signals with their anticipated clinical outcomes. These signals are characterized by a tempo-variant component whose patterns evolve through the recording and an invariant component with patterns that remain unchanged. However, existing SSL methods only drive the model to encode the invariant attributes, leading the model to neglect tempo-variant information which reflects subject-state changes through time. In this paper, we present Parallel-Learning of Invariant and Tempo-variant Attributes (PLITA), a novel SSL method designed for capturing both invariant and tempo-variant ECG attributes. The latter are captured by mandating closer representations in space for closer inputs on time. We evaluate both the capability of the method to learn the attributes of these two distinct kinds, as well as PLITA's performance compared to existing SSL methods for ECG analysis. PLITA performs significantly better in the set-ups where tempo-variant attributes play a major role.

Problem

• Captures both invariant and tempo-variant ECG attributes.
• Addresses neglect of tempo-variant information in existing SSL methods.
• Improves ECG analysis by learning subject-state changes over time.

Innovation

• Parallel-Learning captures invariant and tempo-variant ECG attributes.
• Self-Supervised Learning maps ECG signals to clinical outcomes.
• PLITA improves ECG analysis by considering time-varying patterns.