Ramakrishna Prashanth, P M Nabeel, Kiran V Raj, Joseph Jayaraj, Sivaprakasam Mohanasankar
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2719-2722. doi: 10.1109/EMBC44109.2020.9176739.
The major challenges in deep learning approaches to cuffless blood pressure estimation is selecting the most appropriate representative of the blood pulse waveform and extraction of relevant features for data collection. This paper performs an analysis of a novel dataset consisting of 71 features from the carotid dual-diameter waveforms and 4 blood pressure parameters. In particular, the analysis uses gradient boosting and graph-theoretic algorithms to determine (1) features with high predictive power and (2) potential to be pruned. Identifying such features and understanding their physiological significance is important for building blood pressure estimation models using machine learning that is robust across diverse clinical environments and patient sets.
深度学习方法用于无袖带血压估计的主要挑战在于选择最合适的脉搏波形代表以及提取数据收集的相关特征。本文对一个新数据集进行了分析，该数据集包含来自颈动脉双直径波形的71个特征和4个血压参数。具体而言，该分析使用梯度提升和图论算法来确定：（1）具有高预测能力的特征；（2）可被删减的可能性。识别这些特征并理解其生理意义对于构建在不同临床环境和患者群体中都稳健的机器学习血压估计模型非常重要。
