Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and natural language processing tasks. There is a growing trend to extend unsupervised learning methods to other domains, which helps to utilize a large amount of unlabelled data. This paper proposes an unsupervised pre-training technique based on masked autoencoder (MAE) for electrocardiogram (ECG) signals. In addition, we propose a task-specific fine-tuning to form a complete framework for ECG analysis. The framework is high-level, universal, and not individually adapted to specific model architectures or tasks. Experiments are conducted using various model architectures and large-scale datasets, resulting in an accuracy of 94.39% on the MITDB dataset for ECG arrhythmia classification task. The result shows a better performance for the classification of previously unseen data for the proposed approach compared to fully supervised methods.

翻译：无监督学习方法因其在计算机视觉和自然语言处理任务中展现出对数据集的大规模利用能力和更高准确性，在深度学习领域日益重要。将无监督学习方法扩展到其他领域以利用大量未标记数据的趋势正在增长。本文提出了一种基于掩码自编码器（MAE）的心电图（ECG）信号无监督预训练技术。此外，我们提出了一种任务特定的微调方法，以形成完整的心电图分析框架。该框架具有高层级、通用性，并不针对特定模型架构或任务进行单独适配。使用多种模型架构和大规模数据集进行了实验，在心电图心律失常分类任务中，在MITDB数据集上达到了94.39%的准确率。结果表明，与完全监督方法相比，所提出的方法在对未见数据的分类上表现出更优的性能。