mlsanityData to 14 Oct 2026

CuPID: Leveraging Masked Single-Lead ECG Modelling for Enhancing the Representations

arXiv 2502.21127 ↗First posted 28 Feb 2025cs.LGcited by 0

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Abstract (arXiv)

Wearable sensing devices, such as Electrocardiogram (ECG) heart-rate monitors, will play a crucial role in the future of digital health. This continuous monitoring leads to massive unlabeled data, incentivizing the development of unsupervised learning frameworks. While Masked Data Modelling (MDM) techniques have enjoyed wide use, their direct application to single-lead ECG data is suboptimal due to the decoder's difficulty handling irregular heartbeat intervals when no contextual information is provided. In this paper, we present Cueing the Predictor Increments the Detailing (CuPID), a novel MDM method tailored to single-lead ECGs. CuPID enhances existing MDM techniques by cueing spectrogram-derived context to the decoder, thus incentivizing the encoder to produce more detailed representations. This has a significant impact on the encoder's performance across a wide range of different configurations, leading CuPID to outperform state-of-the-art methods in a variety of downstream tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 28 Feb 2025.

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References · 13

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  12. 2024Contrastive Learning Is Not Optimal for Quasiperiodic Time Seriescited by 4
1 more
  1. 2022Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learningcited by 3

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