WO2021068193A1 - 一种血压波形监测方法及装置 - Google Patents
一种血压波形监测方法及装置 Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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- the function parameter generation model is obtained through training in the following steps:
- the training error is greater than or equal to the preset error threshold, adjust the model parameters of the initial generation model, and return to execute the input of the segmented signal waveform corresponding to the sample physiological signal waveform to the initial function parameter generation
- the model is processed to obtain the prediction function parameter corresponding to the sample physiological signal waveform and the subsequent steps.
- the method for obtaining the parameters of the sample function specifically includes:
- S101 Obtain a physiological signal waveform, and perform signal segmentation processing on the physiological signal waveform to obtain a segmented signal waveform corresponding to the physiological signal waveform; the physiological signal waveform is collected by a signal acquisition device.
- the server 20 generates the function parameters output by the model according to the above-mentioned function parameters, and performs waveform reconstruction by Gaussian reconstruction method or interpolation method, so as to obtain a continuous blood pressure waveform corresponding to the physiological signal waveform.
- the continuous blood pressure waveform obtained by the blood pressure waveform monitoring method provided in this embodiment can be infinitely close to the continuous blood pressure waveform accurately measured by the invasive method based on arterial puncture.
- the statistical measurement errors (mean error ⁇ standard deviation) are all within the acceptable range. That is to say, the blood pressure waveform monitoring method provided by this embodiment can effectively track and monitor the continuous blood pressure waveform and the systolic/diastolic blood pressure.
- Table 1 The processing error obtained by the above statistics is shown in Table 1.
- the function parameters output by the function parameter generation model are the parameters of the fitted Gaussian function of the continuous blood pressure waveform corresponding to the physiological signal waveform, and the Gaussian reconstruction model corresponding to the fitted Gaussian function can be reconstructed by software such as MATLAB.
- Target curve is the continuous blood pressure waveform corresponding to the physiological signal waveform.
- the calculation unit is used to calculate the sample function parameters of the N Gaussian functions according to the fitting result.
- the aforementioned sample physiological signal waveform includes an ECG signal waveform and a pulse signal waveform
- the above-mentioned signal segmentation unit includes a first extraction unit and a second extraction unit.
- the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
- the computer program can be stored in a computer-readable storage medium.
- the computer program can be stored in a computer-readable storage medium.
- the steps of the foregoing method embodiments can be implemented.
- the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate forms.
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Abstract
Description
相关参数 | 系数 | 平均误差± 标准差 |
血压波形 | 0.9371(0.9368-0.9375) | -0.7821±10.9247 |
收缩压 | 0.9405 (0.9358-0.9448) | 0.82 ±10.66 |
舒张压 | 0.8757 (0.8663-0.8845) | 0.61 ± 6.12 |
Claims (20)
- 一种血压波形监测方法,其特征在于,包括:获取生理信号波形,并对所述生理信号波形进行信号分段处理,得到所述生理信号波形对应的分段信号波形;所述生理信号波形由信号采集设备采集得到;将所述生理信号波形对应的分段信号波形输入至已训练的函数参数生成模型进行处理,得到所述生理信号波形对应的函数参数;根据所述函数参数进行波形重构,得到所述生理信号波形对应的连续血压波形。
- 如权利要求1所述的血压波形监测方法,其特征在于,所述函数参数生成模型通过下述步骤训练得到:获取多组训练数据,每组训练数据包括作为训练输入的样本生理信号波形和作为输出的样本函数参数;其中,所述样本函数参数为对连续血压波形进行拟合得到的函数参数,所述连续血压波形与所述样本生理信号波形同步采集得到;分别对所述样本生理信号波形进行信号分段处理,得到所述样本生理信号波形对应的分段信号波形;将所述样本生理信号波形对应的分段信号波形输入至初始函数参数生成模型进行处理,得到所述样本生理信号波形对应的预测函数参数;根据所述样本生理信号波形各自对应的预测函数参数和样本函数参数确定所述初始函数参数生成模型的训练误差;若所述训练误差小于预设误差阈值,则结束训练所述初始函数参数生成模型,并将所述初始函数参数生成模型作为所述已训练的函数参数生成模型;若所述训练误差大于或者等于所述预设误差阈值,则调整所述初始生成模型的模型参数,并返回执行所述将所述样本生理信号波形对应的分段信号波形输入至初始函数参数生成模型进行处理,得到所述样本生理信号波形对应的预测函数参数的步骤以及后续步骤。
- 如权利要求2所述的血压波形监测方法,其特征在于,所述样本函数参数的获取方法,具体包括:采集与所述样本生理信号波形同步的样本连续血压波形;通过N个高斯函数的线性叠加结果对所述样本连续血压波形进行拟合,得到拟合结果;其中,N为正整数;根据所述拟合结果计算所述N个高斯函数的样本函数参数。
- 如权利要求2所述的血压波形监测方法,其特征在于,所述样本生理信号波形包括心电信号波形和/或脉搏信号波形;所述分别对所述样本生理信号波形进行信号分段处理,得到所述样本生理信号波形对应的分段信号波形,包括:检测所述样本生理信号波形的脉搏信号波形的波谷,提取所述脉搏信号波形中相邻两个波谷间的脉搏分段信号波形;检测所述样本生理信号波形的心电信号波形的R波,提取所述心电信号波形的相邻两个R波间的心电分段信号波形。
- 如权利要求1所述的血压波形监测方法,其特征在于,所述根据所述函数参数进行波形重构,得到所述生理信号波形对应的连续血压波形,包括:将所述函数参数输入至高斯重构模型进行处理,得到所述高斯重构模型输出的目标曲线;将所述目标曲线确定为所述生理信号波形对应的连续血压波形。
- 如权利要求5所述的血压波形监测方法,其特征在于,所述高斯重构模型为:curve(t)=a 1e -((t-b1)/c1)2+...+a ie -((t-bi)/ci)2+...+a Ne -((t-bN)/cN)2;其中,curve(t)表示目标曲线;N为高斯函数的个数;{a i,b i,c i}为第i个高斯函数的参数。
- 如权利要求1至6任意一项所述的血压波形监测方法,其特征在于,根据所述函数参数进行波形重构,得到所述生理信号波形对应的连续血压波形之后,还包括:根据所述连续血压波形获取舒张压的数值和/或收缩压的数值。
- 一种血压波形监测装置,其特征在于,包括:第一获取模块,用于获取生理信号波形,并对所述生理信号波形进行信号分段处理,得到所述生理信号波形对应的分段信号波形;所述生理信号波形由信号采集设备采集得到;第二获取模块,用于将所述生理信号波形对应的分段信号波形输入至已训练的函数参数生成模型进行处理,得到所述生理信号波形对应的函数参数;重构模块,用于根据所述函数参数进行波形重构,得到所述生理信号波形对应的连续血压波形。
- 如权利要求8所述的血压波形监测装置,其特征在于,所述函数参数生成模型通过训练模块训练得到,所述训练模块包括:训练数据获取单元,用于获取多组训练数据,每组训练数据包括作为训练输入的样本生理信号波形和作为输出的样本函数参数;其中,所述样本函数参数为对连续血压波形进行拟合得到的函数参数,所述连续血压波形与所述样本生理信号波形同步采集得到;信号分段单元,用于分别对所述样本生理信号波形进行信号分段处理,得到所述样本生理信号波形对应的分段信号波形;输入单元,用于将所述样本生理信号波形对应的分段信号波形输入至初始函数参数生成模型进行处理,得到所述样本生理信号波形对应的预测函数参数;误差单元,用于根据所述样本生理信号波形各自对应的预测函数参数和样本函数参数确定所述初始函数参数生成模型的训练误差;判断单元,用于若所述训练误差小于预设误差阈值,则结束训练所述初始函数参数生成模型,并将所述初始函数参数生成模型作为所述已训练的函数参数生成模型;若所述训练误差大于或者等于所述预设误差阈值,则调整所述初始生成模型的模型参数,并返回执行所述将所述样本生理信号波形对应的分段信号波形输入至初始函数参数生成模型进行处理,得到所述样本生理信号波形对应的预测函数参数的步骤以及后续步骤。
- 如权利要求9所述的血压波形监测装置,其特征在于,所述训练数据获取单元包括:采集单元,用于采集与所述样本生理信号波形同步的样本连续血压波形;拟合单元,用于通过N个高斯函数的线性叠加结果对所述样本连续血压波形进行拟合;其中,N为正整数;计算单元,用于根据拟合结果计算所述N个高斯函数的样本函数参数。
- 一种服务器,包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序,其特征在于,所述处理器执行所述计算机可读指令时实现如下步骤:获取生理信号波形,并对所述生理信号波形进行信号分段处理,得到所述生理信号波形对应的分段信号波形;所述生理信号波形由信号采集设备采集得到;将所述生理信号波形对应的分段信号波形输入至已训练的函数参数生成模型进行处理,得到所述生理信号波形对应的函数参数;根据所述函数参数进行波形重构,得到所述生理信号波形对应的连续血压波形。
- 如权利要求11所述的服务器,其特征在于,所述函数参数生成模型通过下述步骤训练得到:获取多组训练数据,每组训练数据包括作为训练输入的样本生理信号波形和作为输出的样本函数参数;其中,所述样本函数参数为对连续血压波形进行拟合得到的函数参数,所述连续血压波形与所述样本生理信号波形同步采集得到;分别对所述样本生理信号波形进行信号分段处理,得到所述样本生理信号波形对应的分段信号波形;将所述样本生理信号波形对应的分段信号波形输入至初始函数参数生成模型进行处理,得到所述样本生理信号波形对应的预测函数参数;根据所述样本生理信号波形各自对应的预测函数参数和样本函数参数确定所述初始函数参数生成模型的训练误差;若所述训练误差小于预设误差阈值,则结束训练所述初始函数参数生成模型,并将所述初始函数参数生成模型作为所述已训练的函数参数生成模型;若所述训练误差大于或者等于所述预设误差阈值,则调整所述初始生成模型的模型参数,并返回执行所述将所述样本生理信号波形对应的分段信号波形输入至初始函数参数生成模型进行处理,得到所述样本生理信号波形对应的预测函数参数的步骤以及后续步骤。
- 如权利要求12所述的服务器,其特征在于,所述样本函数参数的获取方法,具体包括:采集与所述样本生理信号波形同步的样本连续血压波形;通过N个高斯函数的线性叠加结果对所述样本连续血压波形进行拟合,得到拟合结果;其中,N为正整数;根据所述拟合结果计算所述N个高斯函数的样本函数参数。
- 如权利要求12所述的服务器,其特征在于,所述样本生理信号波形包括心电信号波形和/或脉搏信号波形;所述分别对所述样本生理信号波形进行信号分段处理,得到所述样本生理信号波形对应的分段信号波形,包括:检测所述样本生理信号波形的脉搏信号波形的波谷,提取所述脉搏信号波形中相邻两个波谷间的脉搏分段信号波形;检测所述样本生理信号波形的心电信号波形的R波,提取所述心电信号波形的相邻两个R波间的心电分段信号波形。
- 如权利要求11所述的服务器,其特征在于,所述根据所述函数参数进行波形重构,得到所述生理信号波形对应的连续血压波形,包括:将所述函数参数输入至高斯重构模型进行处理,得到所述高斯重构模型输出的目标曲线;将所述目标曲线确定为所述生理信号波形对应的连续血压波形。
- 一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,其特征在于,所述计算机可读指令被处理器执行时实现如下步骤:获取生理信号波形,并对所述生理信号波形进行信号分段处理,得到所述生理信号波形对应的分段信号波形;所述生理信号波形由信号采集设备采集得到;将所述生理信号波形对应的分段信号波形输入至已训练的函数参数生成模型进行处理,得到所述生理信号波形对应的函数参数;根据所述函数参数进行波形重构,得到所述生理信号波形对应的连续血压波形。
- 如权利要求16所述的计算机可读存储介质,其特征在于,所述所述函数参数生成模型通过下述步骤训练得到:获取多组训练数据,每组训练数据包括作为训练输入的样本生理信号波形和作为输出的样本函数参数;其中,所述样本函数参数为对连续血压波形进行拟合得到的函数参数,所述连续血压波形与所述样本生理信号波形同步采集得到;分别对所述样本生理信号波形进行信号分段处理,得到所述样本生理信号波形对应的分段信号波形;将所述样本生理信号波形对应的分段信号波形输入至初始函数参数生成模型进行处理,得到所述样本生理信号波形对应的预测函数参数;根据所述样本生理信号波形各自对应的预测函数参数和样本函数参数确定所述初始函数参数生成模型的训练误差;若所述训练误差小于预设误差阈值,则结束训练所述初始函数参数生成模型,并将所述初始函数参数生成模型作为所述已训练的函数参数生成模型;若所述训练误差大于或者等于所述预设误差阈值,则调整所述初始生成模型的模型参数,并返回执行所述将所述样本生理信号波形对应的分段信号波形输入至初始函数参数生成模型进行处理,得到所述样本生理信号波形对应的预测函数参数的步骤以及后续步骤。
- 如权利要求17所述的计算机可读存储介质,其特征在于,所述样本函数参数的获取方法,具体包括:采集与所述样本生理信号波形同步的样本连续血压波形;通过N个高斯函数的线性叠加结果对所述样本连续血压波形进行拟合,得到拟合结果;其中,N为正整数;根据所述拟合结果计算所述N个高斯函数的样本函数参数。
- 如权利要求17所述的计算机可读存储介质,其特征在于,所述样本生理信号波形包括心电信号波形和/或脉搏信号波形;所述分别对所述样本生理信号波形进行信号分段处理,得到所述样本生理信号波形对应的分段信号波形,包括:检测所述样本生理信号波形的脉搏信号波形的波谷,提取所述脉搏信号波形中相邻两个波谷间的脉搏分段信号波形;检测所述样本生理信号波形的心电信号波形的R波,提取所述心电信号波形的相邻两个R波间的心电分段信号波形。
- 如权利要求16所述的计算机可读存储介质,其特征在于,所述所述根据所述函数参数进行波形重构,得到所述生理信号波形对应的连续血压波形,包括:将所述函数参数输入至高斯重构模型进行处理,得到所述高斯重构模型输出的目标曲线;将所述目标曲线确定为所述生理信号波形对应的连续血压波形。
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