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基于轮廓和导数的脉搏波量化方法。

The pulse waveform quantification method basing on contour and derivative.

机构信息

Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, P.R. China; School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, P.R. China.

Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, P.R. China; School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, P.R. China.

出版信息

Comput Methods Programs Biomed. 2022 Jun;220:106784. doi: 10.1016/j.cmpb.2022.106784. Epub 2022 Apr 4.

Abstract

OBJECTIVE

Pulse waveform contains abundant physiological and pathological information. The condition of surrounding arteries can be reflected sensitively by the contour and derivative changes of pulse waves. In order to express these changes objectively, the pulse wave needs to be quantified.

METHODS

This study provides a novel quantification method for pulse waveform in the entire cardiac cycle. It involves two new quantification parameters k1 and k2 to display the waveform change caused by the superimposition of wave reflection in the systolic reflex period, which is the most significant changes period. In this method, multi parameters were fused by Kalman filter to obtain an optimal estimation, involving the new parameters and other parameters: k0 for the early systolic period, C1 and C2 for diastole period, and K for pulse pressure.

RESULTS

Use correlation analysis to verify the effectiveness of new parameters that the coefficient is 0.7 between them and the typical augmentation index (AIx). The quantification results of 462 single-cycle pulse waves have consistent change trends with aging in 25-75 different age groups. For respiration analysis, the correlation coefficients are all greater than 0.6, even achieved 0.8 in six multi-cycle data between Kalman optimal estimation and breath wave.

CONCLUSION

This method has quantified the waveform change with physiological status, and these quantification parameters can display the detail of each period.

SIGNIFICANCE

It will be used to verify waveform recognition accuracy and has a vast potential to detect diseases.

摘要

目的

脉搏波包含丰富的生理和病理信息。脉搏波的轮廓和导数变化可以敏感地反映周围动脉的状况。为了客观地表达这些变化,需要对脉搏波进行量化。

方法

本研究提供了一种新的整个心动周期脉搏波的量化方法。它涉及到两个新的量化参数 k1 和 k2,以显示在收缩期反射期内由于波反射叠加引起的波形变化,这是变化最显著的时期。在该方法中,通过卡尔曼滤波器融合多个参数,以获得最佳估计,包括新参数和其他参数:早期收缩期的 k0、舒张期的 C1 和 C2 以及脉搏压的 K。

结果

使用相关分析验证新参数的有效性,其与典型的增强指数(AIx)之间的系数为 0.7。在 25-75 个不同年龄组中,462 个单周期脉搏波的量化结果与年龄增长具有一致的变化趋势。对于呼吸分析,相关系数均大于 0.6,甚至在卡尔曼最优估计值和呼吸波之间的六个多周期数据中达到了 0.8。

结论

该方法已经量化了与生理状态相关的波形变化,这些量化参数可以显示每个周期的详细信息。

意义

它将用于验证波形识别的准确性,并具有检测疾病的巨大潜力。

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