Graduate School of Engineering, Chiba University, Chiba, 263-8522, Japan.
Institute of Fluid Science, Tohoku University, Miyagi, 980-8577, Japan.
Comput Methods Programs Biomed. 2022 Apr;216:106664. doi: 10.1016/j.cmpb.2022.106664. Epub 2022 Jan 29.
Pulse wave has been considered as a message carrier in the cardiovascular system (CVS), capable of inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Clarification and prediction of cardiovascular function by means of powerful feature-abstraction capability of machine learning method based on pulse wave is of great clinical significance in health monitoring and CVDs diagnosis, which remains poorly studied.
Here we propose a machine learning (ML)-based strategy aiming to achieve a fast and accurate prediction of three cardiovascular function parameters based on a 412-subject database of pulse waves. We proposed and optimized an ML-based model with multi-layered, fully connected network while building up two high-quality pulse wave datasets comprising a healthy-subject group and a CVD-subject group to predict arterial compliance (AC), total peripheral resistance (TPR), and stroke volume (SV), which are essential messengers in monitoring CVS conditions.
Our ML model is validated through consistency analysis of the ML-predicted three cardiovascular function parameters with clinical measurements and is proven through error analysis to have capability of achieving a high-accurate prediction on TPR and SV for both healthy-subject group (accuracy: 85.3%, 86.9%) and CVD-subject group (accuracy: 88.3%, 89.2%).
The independent sample t-test proved that our subject groups could represent the typical physiological characteristics of the corresponding population. While we have more subjects in our datasets rather than previous studies after strict data screening, the proposed ML-based strategy needs to be further improved to achieve a disease-specific prediction of heart failure and other CVDs through training with larger datasets and clinical measurements.
Our study points to the feasibility and potential of the pulse wave-based prediction of physiological and pathological CVS conditions in clinical application.
脉搏波被认为是心血管系统（CVS）中的信息载体，能够在诊断心血管疾病（CVDs）的同时推断 CVS 状况。通过基于脉搏波的机器学习方法的强大特征提取能力来阐明和预测心血管功能，对健康监测和 CVDs 诊断具有重要的临床意义，但这方面的研究还很少。
我们提出了一种基于机器学习（ML）的策略，旨在基于 412 个个体的脉搏波数据库快速准确地预测三个心血管功能参数。我们提出并优化了一个基于多层、全连接网络的 ML 模型，同时构建了两个高质量的脉搏波数据集，包括健康组和 CVD 组，以预测动脉顺应性（AC）、总外周阻力（TPR）和心排量（SV），这些都是监测 CVS 状况的重要信使。
我们的 ML 模型通过与临床测量值的一致性分析对三个心血管功能参数进行了验证，并通过误差分析证明，该模型对健康组（准确性：85.3%，86.9%）和 CVD 组（准确性：88.3%，89.2%）的 TPR 和 SV 都具有高精度的预测能力。
独立样本 t 检验证明，我们的实验组可以代表相应人群的典型生理特征。虽然经过严格的数据筛选后，我们的数据集比之前的研究有更多的个体，但为了通过更大的数据集和临床测量值进行训练来实现对心力衰竭和其他 CVDs 的特定疾病预测，我们提出的基于 ML 的策略还需要进一步改进。
我们的研究表明，基于脉搏波的预测在临床应用中具有可行性和潜力，可以用于预测生理和病理 CVS 状况。
