Blood pressure (BP) is a key indicator of cardiovascular health. As hypertension remains a global cause of morbidity and mortality, accurate, continuous, and non-invasive BP monitoring is therefore of paramount importance. Photoplethysmography (PPG) and electrocardiography (ECG) can potentially enable continuous BP monitoring, yet training accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors. Recently, multiple research groups explored Electroencephalographic (EEG)--based foundation models and demonstrated their exceptional ability to learn rich temporal resolution. Considering the morphological similarities between different biosignals, the question arises of whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type. In this work, we take an initial step towards generalized biosignal foundation models by investigating whether model representations learned from abundant EEG data can effectively be transferred to ECG/PPG data solely with fine-tuning, without the need for large-scale additional pre-training, for the BP estimation task. Evaluations on the MIMIC-III and VitalDB datasets demonstrate that our approach achieves near state-of-the-art accuracy for diastolic BP (mean absolute error of 1.57 mmHg) and surpasses by 1.5x the accuracy of prior works for systolic BP (mean absolute error 2.72 mmHg). Additionally, we perform dynamic INT8 quantization, reducing the smallest model size by over 3.5x (from 13.73 MB down to 3.83 MB) while preserving performance, thereby enabling unobtrusive, real-time BP monitoring on resource-constrained wearable devices.

翻译：血压是心血管健康的关键指标。随着高血压持续成为全球发病与死亡的主要原因，准确、连续且无创的血压监测至关重要。光电容积描记法与心电图技术有望实现连续血压监测，然而由于数据质量与患者特异性因素的差异，训练准确且稳健的机器学习模型仍面临挑战。近期，多个研究团队探索了基于脑电图的基础模型，并证明了其在学习丰富时间分辨率方面的卓越能力。考虑到不同生物信号间的形态相似性，一个关键问题随之产生：在单一模态上预训练的模型能否通过有效迁移提升其他信号类型的处理精度？本研究通过探究从海量EEG数据中学习到的模型表征能否仅通过微调（无需大规模额外预训练）有效迁移至ECG/PPG数据以完成血压估计任务，为构建通用生物信号基础模型迈出初步探索。在MIMIC-III与VitalDB数据集上的评估表明：我们的方法在舒张压估计上达到接近最优的精度（平均绝对误差1.57 mmHg），在收缩压估计上以2.72 mmHg的平均绝对误差将现有最佳成果的精度提升1.5倍。此外，我们实施了动态INT8量化，将最小模型尺寸缩减超过3.5倍（从13.73 MB降至3.83 MB）同时保持性能，从而为资源受限的可穿戴设备实现无干扰实时血压监测提供了可能。