用频谱信息增强单导联心电图的无监督学习表示
CuPID: Leveraging Masked Single-Lead ECG Modelling for Enhancing the Representations
- 用频谱上下文引导解码器，提升编码器表征细节
- 在多种下游任务中超越现有最优方法
- 适合做可穿戴设备心电数据无监督建模的研究者
可穿戴心电监测设备将在数字健康未来扮演关键角色，持续监测产生海量无标签数据，推动无监督学习框架发展。尽管掩码数据建模（MDM）技术广泛应用，但其直接用于单导联心电图时效果不佳，因解码器缺乏上下文信息难以处理不规则心跳间隔。本文提出针对单导联心电图的新型MDM方法CuPID，通过引入频谱生成的上下文信息引导解码器，激励编码器生成更细致的表征。该方法在多种配置下显著提升编码器性能，在多个下游任务中优于当前最优方法。
原文摘要 · Abstract (English)
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.
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