摘要
在基于心冲击描记图的非接触式心率检测方法中,心冲击波的真实形态容易在体动发生时被掩盖。为解决无效信号给心跳点定位造成的阻碍,提出一种相空间重构与RBF神经网络结合的体动区间波形补偿模型。首先利用改进的C-C法选取合适的重构参数,并通过动态k-均值聚类确定网络拓扑结构,将动作发生前时间序列在重构空间中的相点作为学习样本输入到模型中,进而实现对无效信号段的单步递归预测。实验结果显示,该预测模型性能良好,能够减少原始信号中不规则噪声带来的影响,经模型修正后计算逐拍心动周期的平均误差为1.27%,平均绝对误差为8.9 ms,有效避免了心跳事件的误判。
In the non-contact heart rate detection method based on ballistocardiogram,the actual shape of ballistocardiogram signals is easily covered up during notable body movements.To address the obstruction caused by invalid signals in locating the heartbeat point,a waveform compensation model for notable movement segments is proposed,which combines phase space reconstruction with RBF neural network.Firstly,the improved C-C method is used to select the appropriate reconstruction parameters.Then,the network topology is determined by dynamic k-means clustering.Transform the time series before the movement into phase points in reconstructed space,and feed them into the model as learning samples.Finally,the single-step recursive prediction of invalid signal segment is realized.Experimental results show that the prediction model has good accuracy and it can reduce the influence of irregular noise in the original signal.After model modification,the mean error of beat by beat cardiac cycle is 1.27%and the mean absolute error is 8.9 ms,effectively avoiding the misjudgment of heartbeat events.
作者
郑小涵
杨越琪
朱岩
李晓欧
ZHENG Xiaohan;YANG Yueqi;ZHU Yan;LI Xiaoou(College of Medical Instruments,Shanghai University of Medicine&Health Sciences,Shanghai 201318,China;College of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《数据采集与处理》
CSCD
北大核心
2023年第4期926-936,共11页
Journal of Data Acquisition and Processing
基金
上海高水平地方高校建设项目(E1-2602-21-201006-1)
上海市智能医疗器械与主动健康协同创新中心建设项目(GWV-10.1-XK05)
上海市科委地方院校能力建设项目(22010502400)。
关键词
相空间重构
RBF神经网络
心冲击描记图
体动区间
波形补偿
phase space reconstruction
RBF neural network
ballistocardiogram(BCG)
movement segment
waveform compensation
