The Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Int J Environ Res Public Health. 2023 Jan 31;20(3):2597. doi: 10.3390/ijerph20032597.
A pulse waveform is regarded as an information carrier of the cardiovascular system, which contains multiple interactive cardiovascular parameters reflecting physio-pathological states of bodies. Hence, multiple parameter analysis is increasingly meaningful to date but still cannot be easily achieved one by one due to the complex mapping between waveforms. This paper describes a new analysis method based on waveform recognition aimed for extracting multiple cardiovascular parameters to monitor public health. The objective of this new method is to deduce multiple cardiovascular parameters for a target pulse waveform based on waveform recognition to a most similar reference waveform in a given database or pattern library.
The first part of the methodology includes building the sub-pattern libraries and training classifier. This provides a trained classifier and the sub-pattern library with reference pulse waveforms and known parameters. The second part is waveform analysis. The target waveform will be classified and output a state category being used to select the corresponding sub-pattern library with the same state. This will reduce subsequent recognition scope and computation costs. The mainstay of this new analysis method is improved dynamic time warping (DTW). This improved DTW and K-Nearest Neighbors (KNN) were applied to recognize the most similar waveform in the pattern library. Hence, cardiovascular parameters can be assigned accordingly from the most similar waveform in the pattern library.
Four hundred and thirty eight (438) randomly selected pulse waveforms were tested to verify the effectiveness of this method. The results show that the classification accuracy is 96.35%. Using statistical analysis to compare the target sample waveforms and the recognized reference ones from within the pattern library, most correlation coefficients are beyond 0.99. Each set of cardiovascular parameters was assessed using the Bland-Altman plot. The extracted cardiovascular parameters are in strong agreement with the original verifying the effectiveness of this new approach.
This new method using waveform recognition shows promising results that can directly extract multiple cardiovascular parameters from waveforms with high accuracy. This new approach is efficient and effective and is very promising for future continuous monitoring of cardiovascular health.
脉搏波被视为心血管系统的信息载体，它包含多个相互作用的心血管参数，反映了身体的生理病理状态。因此，多参数分析在今天变得越来越有意义，但由于波形之间的复杂映射，仍然难以一一实现。本文描述了一种新的基于波形识别的分析方法，旨在提取多个心血管参数来监测公众健康。这种新方法的目的是根据波形识别，从目标脉搏波中推导出多个心血管参数，使其与给定数据库或模式库中的参考波形最相似。
该方法的第一部分包括构建子模式库和训练分类器。这为参考脉搏波和已知参数提供了一个经过训练的分类器和子模式库。第二部分是波形分析。目标波形将被分类，并输出一个状态类别，用于选择具有相同状态的相应子模式库。这将减少后续识别的范围和计算成本。这种新分析方法的主要内容是改进的动态时间规整（DTW）。这种改进的 DTW 和 K-最近邻（KNN）被应用于识别模式库中最相似的波形。因此，可以从模式库中最相似的波形中相应地分配心血管参数。
438 个随机选择的脉搏波被用来验证这种方法的有效性。结果表明，分类准确率为 96.35%。使用统计分析比较目标样本波形和模式库中识别的参考波形，大多数相关系数都超过 0.99。使用 Bland-Altman 图评估每组心血管参数。提取的心血管参数与原始参数高度一致，验证了这种新方法的有效性。
这种使用波形识别的新方法显示出有希望的结果，可以从波形中直接提取高精度的多个心血管参数。这种新方法高效、有效，非常有前途用于未来对心血管健康的连续监测。
