用自监督学习提升心电图分析效率，少标注也能高精度。
HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
- 基于RoBERTa架构，通过自监督学习生成心电图嵌入向量。
- 在小样本数据下表现优于现有模型，参数量更少。
- 适用于医疗信号分析，尤其适合标注数据稀缺场景。
HeartBert模型旨在降低对标注数据的需求、减少计算资源消耗，并提升心电图（ECG）信号机器学习分析系统的性能。受自然语言处理中双向编码器表示（BERT）启发，并结合自监督学习方法，该模型基于RoBERTa架构，生成适用于医疗领域ECG任务的复杂嵌入。为验证其通用性、可迁移性和高效性，选取睡眠阶段检测与心跳分类两个下游任务进行实验。基于HeartBERT的系统采用双向LSTM头，有效应对复杂挑战。一系列实证研究表明，该模型在小规模训练数据、较少学习参数条件下仍能实现优异性能，显著优于对比模型。代码与数据已公开于https://github.com/ecgResearch/HeartBert。
原文摘要 · Abstract (English)
The HeartBert model is introduced with three primary objectives: reducing the need for labeled data, minimizing computational resources, and simultaneously improving performance in machine learning systems that analyze Electrocardiogram (ECG) signals. Inspired by Bidirectional Encoder Representations from Transformers (BERT) in natural language processing and enhanced with a self-supervised learning approach, the HeartBert model-built on the RoBERTa architecture-generates sophisticated embeddings tailored for ECG-based projects in the medical domain. To demonstrate the versatility, generalizability, and efficiency of the proposed model, two key downstream tasks have been selected: sleep stage detection and heartbeat classification. HeartBERT-based systems, utilizing bidirectional LSTM heads, are designed to address complex challenges. A series of practical experiments have been conducted to demonstrate the superiority and advancements of HeartBERT, particularly in terms of its ability to perform well with smaller training datasets, reduced learning parameters, and effective performance compared to rival models. The code and data are publicly available at https://github.com/ecgResearch/HeartBert.
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