CuPID: Leveraging Masked Single-Lead ECG Modelling for Enhancing the Representations
This addresses the challenge of unsupervised learning for massive unlabeled ECG data from wearable devices, with incremental improvements in representation quality.
The paper tackles the problem of suboptimal masked data modeling for single-lead ECG data by introducing CuPID, which cues spectrogram-derived context to the decoder, resulting in outperforming state-of-the-art methods in various downstream tasks.
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.