微弯光纤感知心动周期研究  

Research on Sensing Cardiac Cycles Using a Microbend Fiber Sensor

作　　者：蒋治国 马佳宁 钱淑霞[3] 王超超 张先超 Jiang Zhiguo;Ma Jianing;Qian Shuxia;Wang Chaochao;Zhang Xianchao(Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province,Jiaxing University,Jiaxing 314001,Zhejiang,China;School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China;Department of Neurology,Second Affiliated Hospital of Jiaxing University,Jiaxing 314000,Zhejiang,China;Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province,Jiaxing University,Jiaxing 314001,Zhejiang,China)

机构地区：[1]嘉兴学院浙江省医学电子与数字健康重点实验室,浙江嘉兴314001 [2]北京邮电大学信息与通信工程学院,北京100876 [3]嘉兴学院附属第二医院神经内科,浙江嘉兴314000 [4]嘉兴学院生命健康智能感知浙江省工程研究中心,浙江嘉兴314001

出　　处：《中国激光》2024年第13期204-213,共10页Chinese Journal of Lasers

基　　金：嘉兴市公益性研究计划项目(2022AY30021);浙江省“鲲鹏行动”计划;浙江省教育厅一般科研项目(Y202249280);浙江省自然科学基金(LTGY24F010001)。

摘　　要：心动周期的检测对心血管疾病的诊断和分析有重要意义。该研究通过微弯光纤传感器获取心冲击图(BCG)以进行心动周期检测,主要包括BCG光纤感知、BCG波形提取和BCG特征识别三个过程。针对微弯光纤传感器采集的人体微弱振动信号,提出基于平滑先验滤波联合改进型变分模态分解的BCG波形提取算法,平滑先验滤波可有效抑制低频趋势项,结合中心频率与相关性分析的改进型变分模态分解可有效抑制高频噪声干扰,BCG波形主要特征能够完整保留;提出一种借助幅度特征、峰值时间间隔等先验信息定位特征峰的BCG特征识别算法,采集5位受试者的BCG-心电图(ECG)信号,以ECG的心动周期为标准,通过该算法从BCG中获取心动周期的误差的标准差最大为0.0287 s,平均心率的误差为0.69%,优于短时能量、模板匹配和均值聚类三种特征定位算法。该BCG波形提取和BCG特征识别算法能从微弯光纤传感器获取的微弱心肺振动信号中有效提取心动周期。Objective The detection of the cardiac cycle is essential for the diagnosis and analysis of cardiovascular diseases. In addition to bioelectric signals, a ballistocardiogram(BCG) is another physiological signal that can be used for cardiac cycle detection. Unlike electrocardiograms(ECGs), the collection of BCG signals does not require direct contact with the skin and is safe and noninvasive.The use of microbend fiber optic sensors for BCG sensing has the advantages such as simple structure, low cost, resistance to electromagnetic interference, and high sensitivity. When collecting BCG using a microbend fiber optic sensor, environmental noise,circuit noise, optical path noise, respiratory signals, and motion artifacts affect the performance of the sensor. These noise sources jointly destroy the BCG waveform, and the BCG exhibits nonlinear, nonstationary characteristics and significant individual differences in both the time and frequency domains, thereby affecting the accurate identification of physiological parameters such as the cardiac cycle from the BCG. Therefore, the corresponding signal-processing algorithms should be studied to achieve BCG waveform extraction and feature recognition.Methods The paper investigates the use of a microbend fiber optic sensor to obtain BCGs for cardiac cycle detection, which primarily includes three processes: BCG optical fiber sensing, BCG waveform extraction, and BCG feature recognition. For BCG optical fiber sensing, the microbend fiber optic sensor consists of a multimode optical fiber, grid structure, light source,photodetector, and signal processing circuit, which are embedded in the seat cushion to acquire the heart and lung vibration signals.An algorithm based on the smoothness prior approach(SPA) combined with improved variational mode decomposition(IVMD) is proposed for BCG waveform extraction. Based on the principle of regularized least squares, the SPA is first used to suppress the lowfrequency trend term of the acquired signal. Subsequently, IVMD combined with cent

关 键 词：光纤传感 心冲击图 心动周期 特征识别 

分 类 号：TN29[电子电信—物理电子学]