IEEE J Biomed Health Inform. 2023 May;27(5):2219-2230. doi: 10.1109/JBHI.2022.3181328. Epub 2023 May 4.
Ambulatory blood pressure (BP) monitoring plays a critical role in the early prevention and diagnosis of cardiovascular diseases. However, cuff-based inflatable devices cannot be used for continuous BP monitoring, while pulse transit time or multi-parameter-based methods require more bioelectrodes to acquire electrocardiogram signals. Thus, estimating the BP waveforms only based on photoplethysmography (PPG) signals for continuous BP monitoring has essential clinical values. Nevertheless, extracting useful features from raw PPG signals for fine-grained BP waveform estimation is challenging due to the physiological variation and noise interference. For single PPG analysis utilizing deep learning methods, the previous works depend mainly on stacked convolution operation, which ignores the underlying complementary time-dependent information. Thus, this work presents a novel Transformer-based method with knowledge distillation (KD-Informer) for BP waveform estimation. Meanwhile, we integrate the prior information of PPG patterns, selected by a novel backward elimination algorithm, into the knowledge transfer branch of the KD-Informer. With these strategies, the model can effectively capture the discriminative features through a lightweight architecture during the learning process. Then, we further adopt an effective transfer learning technique to demonstrate the excellent generalization capability of the proposed model using two independent multicenter datasets. Specifically, we first fine-tuned the KD-Informer with a large and high-quality dataset (Mindray dataset) and then transferred the pre-trained model to the target domain (MIMIC dataset). The experimental test results on the MIMIC dataset showed that the KD-Informer exhibited an estimation error of 0.02 ± 5.93 mmHg for systolic BP (SBP) and 0.01 ± 3.87 mmHg for diastolic BP (DBP), which complied with the association for the advancement of medical instrumentation (AAMI) standard. These results demonstrate that the KD-Informer has high reliability and elegant robustness to measure continuous BP waveforms.
动态血压（BP）监测在心血管疾病的早期预防和诊断中起着至关重要的作用。然而，基于袖带的可充气设备不能用于连续 BP 监测，而脉搏传输时间或多参数方法需要更多的生物电极来获取心电图信号。因此，仅基于光电容积脉搏波（PPG）信号估计 BP 波形对于连续 BP 监测具有重要的临床价值。然而，由于生理变化和噪声干扰，从原始 PPG 信号中提取有用特征以进行精细的 BP 波形估计具有挑战性。对于利用深度学习方法进行的单个 PPG 分析，以前的工作主要依赖于堆叠卷积操作，而忽略了潜在的互补时间相关信息。因此，本工作提出了一种基于 Transformer 的新方法，该方法具有知识蒸馏（KD-Informer）功能，可用于 BP 波形估计。同时，我们将通过新颖的反向消除算法选择的 PPG 模式的先验信息集成到 KD-Informer 的知识转移分支中。通过这些策略，模型可以在学习过程中通过轻量级架构有效地捕获有区别的特征。然后，我们进一步采用有效的迁移学习技术，使用两个独立的多中心数据集来证明所提出模型的出色泛化能力。具体来说，我们首先使用大型高质量数据集（Mindray 数据集）对 KD-Informer 进行微调，然后将预训练的模型转移到目标域（MIMIC 数据集）。在 MIMIC 数据集上的实验测试结果表明，KD-Informer 对收缩压（SBP）的估计误差为 0.02±5.93mmHg，对舒张压（DBP）的估计误差为 0.01±3.87mmHg，符合医疗器械促进协会（AAMI）标准。这些结果表明，KD-Informer 具有很高的可靠性和对连续 BP 波形的优雅稳健性。
