ZhongyueZhang785 / Real-Time-Noninvasive-Continuous-Blood-Pressure-Estimation-Using-Machine-LearningView on GitHub
心血管疾病已成为全球范围内致人死亡的头号病因。为了能有效预防心血管疾病，血压的连续测量尤为重要。目前，连续血压测量分为无创测量和有创测量两种方式。有创测量虽然能达到较高的精度，但是操作复杂且存在感染风险。无创测量主要基于脉搏波。随着机器学习的发展，愈来愈多的人使用脉搏波特征参数法。该方法主要存在两点问题。其一，手动提取特征对波形的要求较高，特征选取受研究者先验知识影响，极有可能提取到非相关特征。其二，血压波形中包含的丰富生理信息未能被充分挖掘。大多数研究的预测目标为收缩压、舒张压等单一血压值，较少的研究关注血压整体波形的预测。 针对上述问题，本文创新性地将原本用于二维图像处理的U-Net模型引入一维血压预测中，提出了一种基于U-Net的PPG-ABP转换模型。该方法无需手动提取特征，仅…
☆17Feb 14, 2022Updated 4 years ago
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