QECGBERT：用 VQ-VAE 波形分词构建心电基础模型
QECGBERT: A Scalable ECG Foundation Model Based on Self-Supervised Language Learning via VQ-VAE Waveform Tokenization
这篇论文把心电图当语言来处理，用 VQ-VAE 把波形切成 token 再跑 BERT 预训练，80 万条 MIMIC-IV 数据训练出来的，做心电诊断方向的可以看看。
QECGBERT 是一个自监督心电（ECG）语言模型，通过 VQ-VAE 将 12 导联心电图的 P、QRS、T 等波形分词，把生理结构显式编码进 token。分词后的短序列让 BERT 掩码预训练更紧凑，计算成本低于长序列方案。模型在包含超过 80 万条无标注心电记录的 MIMIC-IV 数据集上预训练，并在 PTB-XL、CPSC2018、Chapman-Shaoxing-Ningbo 三个数据集上微调评估，成绩达到或超过已发布基准。
QECGBERT: A Scalable ECG Foundation Model Based on Self-Supervised Language Learning via VQ-VAE Waveform Tokenization
AI foundation models play a pivotal role in advancing heart disease diagnosis from electrocardiogram (ECG) data, particularly for rare diseases where the lack of sufficient labeled data hinders supervised learning. However, most self-supervised learning methods treat ECGs as generic sequence or image data without exploiting underlying physiological structure. To address this limitation, we propose QECGBERT, a self-supervised ECG language model that explicitly encodes physiological semantics by tokenizing the P, QRS, T, and isoelectric background waveforms. Vector-quantized variational autoencoder (VQ-VAE) tokenization is applied to each 12-lead ECG, with tokens enriched by continuous waveform features to preserve morphology lost during quantization without increasing sequence length. This design enables compact BERT masked language modeling with short sequences, improving scalability and reducing computational cost relative to long-sequence approaches. QECGBERT applies BERT pre-training to the MIMIC-IV dataset containing over 800,000 unlabeled ECG recordings, and fine-tuning on labeled PTB-XL, CPSC2018, and Chapman--Shaoxing--Ningbo datasets for evaluation. The results demonstrate that QECGBERT achieves comparable or superior performance to published benchmarks, offering a scalable, physiologically aware approach to ECG foundation modeling for cardiac diagnosis.