PDF(Pubmed)
Abstract：
Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network\'s representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.
摘要：
动脉血压（ABP）是心血管健康评估中的关键临床指标。随着连续血压的精确预测在预防和治疗心血管疾病中发挥关键作用。本研究提出了一种新的连续无创血压预测模型,DSRUnet,基于深度稀疏残差U网结合改进的SE跳过连接，旨在提高使用光电容积描记术（PPG）信号进行连续血压预测的准确性。该模型首先引入了路径收缩和扩展的稀疏残差连接方法，促进更丰富的信息融合和特征扩展，以更好地捕获原始PPG信号中的细微变化，从而增强网络的表示能力和预测性能，并减轻网络性能的潜在下降。此外，增强的SE-GRU模块被嵌入在跳过连接中，以使用注意机制对全局信息进行建模和加权，通过GRU层捕获PPG脉搏信号的时间特征,以提高传递的特征信息的质量,减少冗余特征学习。最后,解码器模块中包含了一种深度监督机制，以指导下层网络学习有效的特征表示，缓解梯度消失的问题，促进网络的有效训练。所提出的DSRUnet模型在公开可用的UCI-BP数据集上进行了训练和测试，预测收缩压(SBP)的平均绝对误差，舒张压（DBP），平均血压（MBP）为3.36±6.61mmHg，2.35±4.54mmHg，2.21±4.36mmHg，分别,符合医疗器械促进协会（AAMI）设定的标准，根据英国高血压协会（BHS）SBP和DBP预测标准，达到A级。通过消融实验和与其他先进方法的比较，DSRUnet在血压预测任务中的有效性，特别是对于SBP，通常会产生较差的预测结果，明显更高。实验结果表明,DSRUnet模型能够准确地利用PPG信号进行实时连续血压预测,获得高质量、高精度的血压预测波形。由于其非侵入性，连续性,和临床相关性，该模型可能对医院的临床应用和日常生活中可穿戴设备的研究具有重要意义。
