A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring.
A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring.
复制标题
DOI：
10.3390/s17040890
复制
发表时间：
2017-04-18
期刊：
影响因子：
--
通讯作者：
Nazeran H
中科院分区：
文献类型：
--
作者：
Martinek R;Nedoma J;Fajkus M;Kahankova R;Konecny J;Janku P;Kepak S;Bilik P;Nazeran H
本论文的重点是设计、实现和验证一种新型的基于心音图的光纤传感器和自适应信号处理系统，用于无创连续胎儿心率（fHR）监测。我们提出的系统利用两个马赫-曾德尔干涉传感器。基于对真实的测量数据的分析，我们建立了一个简化的心音在人体内产生和分布的动力学模型。在此信号模型的基础上，我们设计，实现，并验证了我们的自适应信号处理系统，实现两个随机梯度为基础的算法：最小均方算法（LMS），和归一化最小均方（NLMS）算法。利用该系统，我们能够从高质量的胎儿心音图（fPCG）中提取fHR信息，通过执行fPCG信号峰值检测从腹部母体心音图（mPCG）中过滤。常见的信号处理方法，如线性滤波、信号减法等，不能用于此目的，因为fPCG和mPCG信号共享重叠的频谱。通过使用定性（妇科研究）和定量测量来评估自适应系统的性能，例如：信噪比-SNR、均方根误差-RMSE、灵敏度-S+和阳性预测值-PPV。
This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.