基于Transformer模型的连续无创血压预测方法

Continuous non-invasive blood pressure prediction method based on Transformer

动脉血压(ABP)波形包含丰富的心血管信息,有助于心血管疾病的预防和诊断。目前大部分基于光电容积脉搏波描记法(PPG)的血压预测方法仅预测收缩压(SBP)和舒张压(DBP),本文提出了一种由PPG信号预测ABP波形的血压测量方法。首先使用PPG信号作为输入,通过线性映射到高维空间,然后利用Transformer编码器结构进行特征提取,最后由线性层输出预测的ABP波形,由预测的ABP波形可计算出SBP和DBP等血压参数。实验结果显示,Transformer网络在MIMIC数据集中预测的ABP波形与实际波形的拟合效果良好,计算得到的SBP和DBP预测误差分别为(3.76±5.66)mmHg、(2.20±3.77)mmHg,且该方法符合美国医疗仪器促进协会(AAMI)的标准,同时在英国高血压协会(BHS)标准中达到A级。

Rich cardiovascular information is encompassed within the arterial blood pressure(ABP)waveform,offering valuable insights for the prevention and diagnosis of cardiovascular diseases.Despite the availability of several photoplethysmography(PPG)-based blood pressure prediction methods,they primarily focus on predicting systolic blood pressure(SBP)and diastolic blood pressure(DBP).This paper proposes a novel method for blood pressure measurement that predicts the entire ABP waveform from PPG signals.The proposed approach involves linearly mapping the PPG signal to a high-dimensional space and feature extraction using a Transformer encoder structure.A linear layer is then utilized to output the predicted ABP waveform,enabling the calculation of SBP and DBP.Experimental results demonstrate that the Transformer network provides an accurate fit to the actual ABP waveform in the MIMIC dataset,with predicted SBP and DBP errors averaging(3.76±5.66)mmHg and(2.20±3.77)mmHg,respectively.Additionally,the proposed method complies with the standards of the Association for the Advancement of Medical Instrumentation(AAMI)and achieves Grade A according to the British Hypertension Society(BHS)criteria.