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.

翻译：可穿戴传感设备，如心电图（ECG）心率监测器，将在数字健康的未来发挥关键作用。这种持续监测产生了海量未标记数据，推动了无监督学习框架的发展。尽管掩码数据建模（MDM）技术已得到广泛应用，但由于解码器在缺乏上下文信息时难以处理不规则的心跳间隔，将其直接应用于单导联ECG数据效果欠佳。本文提出了一种名为“提示预测器以增强细节”（CuPID）的新型MDM方法，专为单导联ECG设计。CuPID通过向解码器提供源自频谱图的上下文提示，增强了现有MDM技术，从而激励编码器生成更精细的表征。这对编码器在多种不同配置下的性能产生了显著影响，使得CuPID在各类下游任务中超越了现有最先进方法。