Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy.
Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland.
Sensors (Basel). 2024 Jun 6;24(11):3697. doi: 10.3390/s24113697.
Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and 1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.
理解影响血压控制的调节机制对于持续监测该参数至关重要。利用数据驱动的特征实施个性化机器学习模型为跟踪各种情况下的血压波动提供了机会。在这项工作中，从 28 名健康受试者的肱动脉和指动脉中提取的数据驱动光电容积脉搏波特征被用于为随机森林分类器提供输入，试图开发一种能够跟踪血压的系统。我们根据所使用的训练集的不同大小和个性化程度评估了后者分类器的行为。当将目标受试者的 30%脉搏波与数据集中的五个随机选择的源受试者的脉搏波组合时，综合准确率、精确率、召回率和 1 分率分别达到 95.1%、95.2%、95%和 95.4%。实验结果表明，在认知或体力工作负荷条件下，结合来自不同受试者的预训练阶段的数据，可以区分逐拍脉搏波的形态差异。
