Graduate School of Science and Engineering, Chiba University, Chiba, Japan.
Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan.
Biomed Eng Online. 2024 Jan 19;23(1):7. doi: 10.1186/s12938-024-01201-7.
Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland-Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.
脉搏波作为心血管系统 (CVS) 中的信息载体，可用于推断 CVS 状况，从而诊断心血管疾病 (CVD)。心力衰竭 (HF) 是一种主要的 CVD，通常需要昂贵且耗时的治疗方法来进行健康监测和疾病恶化；通过基于脉搏波的机器学习 (ML) 的强大特征提取能力，为快速准确地评估心脏的血液供应能力提供了一种有效且适合患者的工具，这方面的研究仍有待开展。在这里，我们提出了一种基于 ML 的方法，该方法已通过 237 名患者的临床数据得到验证，可以准确评估 HF 患者的血液供应能力，能够快速预测包括左心室射血分数 (LVEF)、左心室舒张末期直径 (LVDd)、左心室收缩末期直径 (LVDs)、左心房内径 (LAD) 和外周血氧饱和度 (SpO) 在内的五个代表性心血管功能参数。我们使用了两种 ML 网络，并基于高质量的脉搏波数据集进行了优化，通过基于汇总独立样本 t 检验 (p > 0.05)、与临床测量值的 Bland-Altman 分析以及误差函数分析的统计分析，对其进行了一致性验证。结果证明，SpO、LAD 和 LVDd 的评估性能可以达到最大误差 < 15%。虽然我们的研究结果表明了基于脉搏波的 HF 患者血液供应能力的无创评估具有潜力，但它们也为进一步完善健康监测和恶化预防应用奠定了基础。
