IEEE Trans Biomed Eng. 2023 Jul;70(7):2139-2148. doi: 10.1109/TBME.2023.3236918. Epub 2023 Jun 19.
The clinical significance of the wave intensity (WI) analysis for the diagnosis and prognosis of the cardiovascular and cerebrovascular diseases is well-established. However, this method has not been fully translated into clinical practice. From practical point of view, the main limitation of WI method is the need for concurrent measurements of both pressure and flow waveforms. To overcome this limitation, we developed a Fourier-based machine learning (F-ML) approach to evaluate WI using only the pressure waveform measurement.
Tonometry recordings of the carotid pressure and ultrasound measurements for the aortic flow waveforms from the Framingham Heart Study (2640 individuals; 55% women) were used for developing the F-ML model and the blind testing.
Method-derived estimates are significantly correlated for the first and second forward wave peak amplitudes (Wf1, r = 0.88, p 0.05; Wf2, r = 0.84, p 0.05) and the corresponding peak times (Wf1, r = 0.80, p < 0.05; Wf2, r = 0.97, p 0.05). For backward components of WI (Wb1), F-ML estimates correlated strongly for the amplitude (r = 0.71, p 0.05) and moderately for the peak time (r = 0.60, p 0.05). The results show that the pressure-only F-ML model significantly outperforms the analytical pressure-only approach based on the reservoir model. In all cases, the Bland-Altman analysis shows negligible bias in the estimations.
The proposed pressure-only F-ML approach provides accurate estimates for WI parameters.
The pressure only F-ML approach introduced in this work expand the clinical usage of WI into inexpensive and non-invasive settings such as wearable telemedicine.
波强（WI）分析在心血管疾病诊断和预后中的临床意义已得到充分证实。然而，这种方法尚未完全转化为临床实践。从实际角度来看，WI 方法的主要局限性是需要同时测量压力和流量波形。为了克服这一限制，我们开发了一种基于傅里叶的机器学习（F-ML）方法，仅使用压力波形测量来评估 WI。
使用弗雷明汉心脏研究（Framingham Heart Study）的颈动脉压力和主动脉流量超声测量的张力记录（2640 人；55%为女性）来开发 F-ML 模型和盲测试。
方法衍生的估计值与第一和第二前向波峰值幅度（Wf1，r = 0.88，p 0.05；Wf2，r = 0.84，p 0.05）和相应的峰值时间（Wf1，r = 0.80，p 0.05；Wf2，r = 0.97，p 0.05）显著相关。对于 WI 的反向分量（Wb1），F-ML 估计值在幅度上强烈相关（r = 0.71，p 0.05），在峰值时间上中度相关（r = 0.60，p 0.05）。结果表明，仅压力的 F-ML 模型显著优于基于储层模型的仅压力分析方法。在所有情况下，Bland-Altman 分析显示估计值的偏差可忽略不计。
提出的仅压力 F-ML 方法可为 WI 参数提供准确估计。
本研究引入的仅压力 F-ML 方法将 WI 的临床应用扩展到经济实惠且非侵入性的环境中，例如可穿戴远程医疗。
