基于GA优化的MIV-BP神经网络连续血压无创监测方法研究
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摘要
针对现有的基于脉搏波传导时间法或脉搏波特征参数法的血压测量模型存在的不足,提出利用平均影响值(Mean Impact Value,MIV)法从提取的脉搏波传导时间和脉搏波特征参数中优选出对血压值影响较大的参数作为输入量,血压值作为输出量训练BP神经网络模型,然后采用遗传算法(Genetic Algorithm,GA)对个性化参数进行优化,从而建立一种连续血压无创监测模型—GA-MIV-BP神经网络模型。该模型计算血压的结果与实际测量得到的结果进行Bland-Altman一致性分析,表明两者具有很好的一致性,可互换使用,因此该算法对促进无创连续血压监测方法的临床应用具有积极作用。
Aiming at shortcomings of existing blood pressure measurement models based on the pulse wave transit time method or the pulse wave feature parameter method, a non-invasive continuous blood pressure monitoring method based on GA-MIV-BP neural network model was proposed. The factors obviously affecting blood pressure were chosen from the extracted pulse wave transit time and pulse wave feature parameters using the mean impact value(MIV) method. These factors were taken as inputs and actual blood pressure values were taken as outputs to train a BP neural network model, and the personalized parameters were optimized using the genetic algorithm(GA) to establish GA-MIV-BP neural network model. Bland-Altman consistency analysis was performed for the blood pressure calculation results using the proposed method and the actual blood pressure measurement ones. It was shown that both of them have good consistency and are interchangeable to use, so the proposed method is very helpful for promoting the clinical application of non-invasive continuous blood pressure monitoring.
Aiming at shortcomings of existing blood pressure measurement models based on the pulse wave transit time method or the pulse wave feature parameter method, a non-invasive continuous blood pressure monitoring method based on GA-MIV-BP neural network model was proposed. The factors obviously affecting blood pressure were chosen from the extracted pulse wave transit time and pulse wave feature parameters using the mean impact value(MIV) method. These factors were taken as inputs and actual blood pressure values were taken as outputs to train a BP neural network model, and the personalized parameters were optimized using the genetic algorithm(GA) to establish GA-MIV-BP neural network model. Bland-Altman consistency analysis was performed for the blood pressure calculation results using the proposed method and the actual blood pressure measurement ones. It was shown that both of them have good consistency and are interchangeable to use, so the proposed method is very helpful for promoting the clinical application of non-invasive continuous blood pressure monitoring.
引文
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[16] 罗小刚,彭承琳,郑小林,等.基于小波变换与形态学运算的ECG综合检测算法的研究[J].生物医学工程学杂志,2004,21(5):788-790.LUO Xiaogang,PENG Chenglin,ZHENG Xiaolin,et al.A study of ECG combination detection algorithm based on wavelet transform and morphological method[J].Journal of Biomedical Engineering,2004,21(5):788-790.
[17] 庄育锋,胡晓瑾,翟宇.基于BP神经网络的微量药品动态称重系统非线性补偿[J].仪器仪表学报,2014,35(8):1914-1920.ZHUANG Yufeng,HU Xiaojin,ZHAI Yu.Nonlinear compensation of micro scale capsule dynamic condition weighing unit based on BP neural network model[J].Chinese Journal of Scientific Instrument,2014,35(8):1914-1920.
[18] HUYNH H T,WON Y.Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks[J].Pattern Recognition Letters,2011,32(14):1930-1935.
[19] MOLLER M F.A scaled conjugate gradient algorithm for fast supervised learning[J].Neural Network,1993,6(4):525-533.
[20] AMIN Y H M,KEIVAN M,SIMA J.A new approach for blood pressure monitoring based on ECG and PPG signals by using Artificial Neural Networks[J].International Journal of Computer Applications,2014,103(12):36-40.
[2] 蒋巍巍,季忠.无创血压测量方法的研究进展[J].中华高血压杂志,2015,23(7):685-689.JIANG Weiwei,JI Zhong.Research progress of noninvasive blood pressure measurement methods[J].Chinese Journal of hypertension,2015,23(7):685-689.
[3] SLIKE B,MCAULEY D.Accuracy and precision of blood pressure determination with the Finapres:an overview using re-sampling statistics[J].J Hum Hypertens,1998,12(6):403-409.
[4] THOMAS S S,NATHAN V,ZONG C Z,et al.Bio watch:a non-invasive wrist-based blood pressure monitor that incorporates training techniques for posture and subject variability[J].IEEE Journal of Biomedical and Health Informatics,2016,20(5):1291-1300.
[5] 李顶立.基于脉搏波的无创连续血压测量方法研究[D].杭州:浙江大学,2008.
[6] 焦学军,房兴业.利用脉搏波特征参数连续测量血压的方法研究[J].生物医学工程学杂志,2002(2):217-220.JIAO Xuejun,FANG Xingye.Research on continuous measurement of blood pressure via characteristic parameters of pulse wave[J].Journal of Biomedical Engineering,2002(2):217-220.
[7] 徐可欣,王继寸,余辉,等.脉搏波时域特征与血压相关性的研究[J].中国医疗设备,2009(8):42-45.XU Kexin,WANG Jicun,YU Hui,et al.Research of correlation between time-domain characteristics of the pulse wave and blood pressure[J].China Medical Devices,2009(8):42-45.
[8] 董锋.基于脉搏波特征的连续血压测量方法研究[D].昆明:云南大学,2015.
[9] LIU Xu,JI Zhong,TANG Yuran.Recognition of pulse wave feature points and non-invasive blood pressure measurement[J].Journal of Signal Processing Systems,2017,87(2):241-248.
[10] 向海燕.无创伤人体逐拍动脉血压测量技术[D].西安:第四军医大学,2005.
[11] 梁妃学,姚翔,于巍,等.双通道脉搏波速度测量系统的研制[J].中国医学物理学杂志,2006,23(3):209-213.LIANG Feixue,YAO Xiang,YU Wei,et al.The development of dual channel pulse wave velocity measurement system[J].Chinese Journal of Medical Physics,2006,23(3):209-213.
[12] 何芳,王小川,肖森予,等.基于MIV-BP型网络实验的房地产项目风险识别研究[J].运筹与管理,2013,22 (2):229-234.HE Fang,WANG Xiaochuan,XIAO Senyu,et al.Research on risk recognition of real estate projects based on MIV-BP neural network test[J].Operations Research and Management Science,2013,22 (2):229-234.
[13] 夏玫.BP神经网络泛化能力改进研究[D].太原:太原科技大学,2006.
[14] 胥永刚,赵国亮,马朝永,等.基于双树复小波与非线性时间序列的降噪方法[J].振动与冲击,2015,34(16):135-140.XU Yonggang,ZHAO Guoliang,MA Chaoyong,et al.New denoising method based on dual-tree complex wavelet transform and nonlinear time series[J].Journal of Vibration and Shock,2015,34(16):135-140.
[15] 查玉华,李巍,陈文霞.基于小波变换和样条插值的容积脉搏波去噪方法研究[J].中国医学装备,2015,12(10):27-29.ZHA Yuhua,LI Wei,CHEN Wenxia.A method of plethysmography wave de-noising based on wavelet transform and spline interpolation[J].China Medical Equipment,2015,12(10):27-29.
[16] 罗小刚,彭承琳,郑小林,等.基于小波变换与形态学运算的ECG综合检测算法的研究[J].生物医学工程学杂志,2004,21(5):788-790.LUO Xiaogang,PENG Chenglin,ZHENG Xiaolin,et al.A study of ECG combination detection algorithm based on wavelet transform and morphological method[J].Journal of Biomedical Engineering,2004,21(5):788-790.
[17] 庄育锋,胡晓瑾,翟宇.基于BP神经网络的微量药品动态称重系统非线性补偿[J].仪器仪表学报,2014,35(8):1914-1920.ZHUANG Yufeng,HU Xiaojin,ZHAI Yu.Nonlinear compensation of micro scale capsule dynamic condition weighing unit based on BP neural network model[J].Chinese Journal of Scientific Instrument,2014,35(8):1914-1920.
[18] HUYNH H T,WON Y.Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks[J].Pattern Recognition Letters,2011,32(14):1930-1935.
[19] MOLLER M F.A scaled conjugate gradient algorithm for fast supervised learning[J].Neural Network,1993,6(4):525-533.
[20] AMIN Y H M,KEIVAN M,SIMA J.A new approach for blood pressure monitoring based on ECG and PPG signals by using Artificial Neural Networks[J].International Journal of Computer Applications,2014,103(12):36-40.