Fetal ECG Extraction Based on Adaptive Linear Neural Network

Fetal ECG extraction has the vital significance for fetal monitoring. This paper introduces a method of extracting fetal ECG based on adaptive linear neural network. The method can be realized by training a small quantity of data. In addition, a better result can be achieved by improving neural network structure. Thus, more easily identified fetal ECG can be extracted. Experimental results show that the adaptive linear neural network can be used to extract fetal ECG from maternal abdominal signal effectively. What's more, a clearer fetal ECG can be extracted by improving neural network structure.     

基于扩展卡尔曼滤波和奇异值分解算法的单通道胎儿心电提取方法

提出一种将扩展卡尔曼滤波(EKF)算法和奇异值分解(SVD)算法相结合的单通道胎儿心电提取方法。首先,建立母体心电的动态模型,利用该模型通过扩展卡尔曼滤波或扩展卡尔曼平滑(EKS),从孕妇的单通道腹部信号中估计出母体心电成分,然后与单通道腹部信号相减得到胎儿心电信号的初步估计,随后再利用奇异值分解算法,对初步估计出的胎儿心电信号进行去噪处理,以期得到高信噪比的胎儿心电信号。另外,针对胎儿心律不齐的情况,在奇异值分解算法中提出一种改进的心电信号重构矩阵构造方法。对合成腹部信号和实际腹部信号(源于DaISy数据库和PhysioNet中的非侵入式胎儿心电数据库,共计49个腹部通道的数据),进行胎儿心电提取实验。结果表明,使用EKF+SVD或EKS+SVD的算法比单独使用EKF或EKS的算法,提取出的胎儿心电信号的信噪比提高约5 dB,胎儿心电提取的准确性分别达95.60%和95.94%。结合EKF和SVD算法的单通道胎儿心电提取方法,可以有效地提高胎儿心电信号的信噪比和提取的准确性,并且适用于母体或胎儿心律不齐的情况。     

基于自适应线性神经网络的胎儿心电信号提取

胎儿心电信号提取对胎儿监护具有重要意义。本文介绍了一种基于自适应线性神经网络的胎儿心电信号提取方法。该方法根据母体心电信号与母体腹部信号的相关性原理,以母体心电信号为网络输入,母体腹部信号为网络目标,采用W-H学习方法获取的训练误差即为提取出的胎儿心电信号。此外,通过增加网络隐含层,对神经网络的结构进行改进,增加网络训练精度,从而得到更好的训练结果,提取出更易识别的胎儿心电信号。最后分别使用仿真数据和临床数据对上述方法进行测试,实验结果表明,利用自适应线性神经网络可以提取出胎儿心电信号,通过改进神经网络结构,可以提取出更为清晰的胎儿心电信号。     

细胞膜离子单通道信号的频域分析方法

利用离散小波变换构造尺度能量分布函数 (Power distribution function,PDF) ,分析单通道信号的频域特征 ;同时采用功率密度谱 (Power spectral density,PSD)方法进行分析。结果表明 ,基于离散小波变换 (Discretewavelet transform,DWT)的尺度能量分布是一种非常有效的单通道信号的频域分析方法。     

基于小波分析方法的脑电诱发电位单导少次提取

脑电诱发电位(EP)的单导少次提取具有重要的临床意义.本研究提出一种基于小波分析的方法,用于EP信号的单导少次提取.该算法首先将检测得到的信号经过白化滤波器进行滤波,将自发脑电预白化为白噪声,目的是使噪声的小波系数均匀分布在所有尺度和位移上,而期望信号的小波系数只在少数尺度上为非零值.进而对滤波后的信号的小波系数进行加权处理,再通过反变换得到期望信号,恢复出EP信号.在不同初始信噪比条件下仿真,进行算法测试和性能分析.结果表明,该算法能够抑制自发脑电的干扰,使信噪比提高13 dB,相当于20次叠加平均效果,从而可以实现EP信号的单导快速提取.     

基于虚拟通道ICA方法的诱发电位单导少次提取

脑电诱发电位的单导少次提取一直是生物医学信号处理领域倍受关注的问题。独立分量分析作为解决盲源分离问题的一种有效算法已被广泛应用于诱发电位提取之中。独立分量分析处理的是多路观测信号,且要求观测信号路数大于或等于独立信号源的个数。为了能够应用独立分量分析算法实现诱发电位的单导少次提取,引入虚拟通道构建观测信号矩阵,从而得到符合实际应用条件的算法模型。4路信号仿真实验表明了虚拟通道模型可以有效提取诱发电位。对12位受试者进行模式翻转视觉诱发电位测试,仅用单导连续4次记录即可实现诱发电位的初步提取,信噪比增加约为12 dB,当采用10路虚拟通道,信噪比提高约20 dB。4路和10路虚拟通道ICA方法下得到的多导联VEP相关系数的统计结果进一步证实增加虚拟通道的数量,EP信号提取效果也会更好。     

A Single Channel Measurement System For Brain MIT

INTRODUCTION　　 It is imperative for brain surgeons to have a brain image monitor which is non-contact,non-invasive,minitype and continuous imaging at present.The magneticinduction tomography( MIT) gives information of the electrical characteristics ofbrain and is a prospective method to meetbrain surgeons' requirements of the moni-toring equipment.MIT applies an alternative magnetic field from an exciting coil toinduce eddy currents in the head and the magnetic field from eddy currents…     

胎儿心电微机监护装置

为了对孕妇产程中的胎儿进行有效的电子监护 ,研究了基于胎儿头皮心电信号特征 ,利用自适应抵消器和斜率峰值双阈值检测技术对信号进行了处理 ,采用可变积分器来排除因导联切换 ,导联脱落所引起的基线漂移 ,设计了基于Cygnal公司C80 5 1F0 2 0单片机的胎儿心电内监护装置。     

便携式心电监护中实时信号处理方法研究

针对心电信号处理过程中的心电信号数字滤波、心电波形的动态显示、心电数据存储等问题,阐述了3个可用于心电信号实时处理的方法:一是运用滤波器频谱的周期性减少了滤波器系数个数,提高了运算速度,并根据卷积公式特点实现了数字滤波的实时性;二是运用基于内存虚拟屏幕技术实现心电波形动态显示,解决了屏幕闪烁和绘图不连续问题;三是采用嵌入式数据库SQLITE实现了心电数据存储.所有方法均考虑实时性要求,并已成功用于课题组开发的便携式心电监护仪,效果较为理想,具有很强的实用价值.     

便携式心电监护系统中心电信号的实时分析方法设计

目的：设计并实现一种适用于便携式心电监护系统的心电波形实时动态检测和分析的方法。方法 ：作者首先应用5点平滑滤波消除信号的高频噪声和50 Hz干扰,然后通过对分段心电信号的长度变换来增强R波,并用长度阈值检测到R波位置,再通过去错检和查漏检算法提高R波检测准确率;正确检测到R波后,利用区域极值和斜率突变特点从R波开始向前、向后搜索找出Q、S波,然后从已开始的Q、S波位置再分别向前向后找到Q波起点和S波终点;最后根据已检测到的QRS波群计算了心率和ST段参数。结果：通过对包含各种噪声的心电信号的分析证明该算法能准确地检测到QRS波群,不受基线漂移和高频噪声的影响;算法用C语言实现后在嵌入式心电监护系统中的应用也表明其处理速度完全满足移动设备的实时动态分析要求。结论：本文设计的心电波形识别方法算法简单、速度快、抗干扰能力强、准确率高,并成功应用于基于32位嵌入式系统的心电监护仪。相信能给便携式心电监护设备研发中心电信号自动检测和分析功能的实现带来一些启发。     

Changes in the Fetal Heart Rate and ECG during Hypoxia

Previous reports on the fetal hypoxic bradycardia in animals have indicated, that there is a vagal influence, especially when asphyxia is induced by umbilical cord occlusion. In the present study hypoxia was induced oia the mother, thus keeping the fetal circulation intact. The experiments were conducted on mature fetuses of three different species, namely 20 guinea-pig, 3 cat and 3 lamb fetuses. The ECG was recorded continuously and used for measuring the fetal heart rate. The vagal influence on the fetal hypoxic bradycardia was tested by comparing the time for the onset of the bradycardia with or without vapl activity. There was no indication of any vagal component in the fetal hypoxic bradycardia, which is therefore to be regarded rather as a sign of myocardial hypoxia and failing fetal circulation. The ECG recordings showed that the fetal bradycardia initially is an AV-block, type II, and that there are progressive changes in the S-T interval as an early sign of hypoxia.     

心房活动电生理信号特征提取

为从12导联体表心电图中有效地提取反映心房活动的电生理信号,构造一种稀疏表示下的独立成分分析模型。首先,对采集到的12导联体表心电信号进行预处理,然后通过小波变换,实现心电信号小波域的稀疏表示形式,对变换后的心电信号进行独立成分分析,并通过频谱分析确定出反映心房活动的电生理信号源。讨论了平均房颤周长的这一测量指标在房颤患者中的具体应用。实验表明,该方法可以有效提取心房活动的电生理信号,将对心房活动电生理的深入研究产生积极影响。     

小波变换在心电信号特征提取中的应用

采用分段阈值和模极大值对斜率判据相结合的补偿策略,提出了一种精确提取QRS波群特征值的算法.经过对MIT/BIH心电数据库和临床实测的心电信号的大量实验,结果显示即使在有严重噪声干扰的情况下,运用本算法也很容易实现对QRS波群特征的有效提取,特别是对R波峰具有相当高的定位精度(其误差不超过一个采样点)和分析精度(没有累积误差).     

基于单细胞动作电位仿真心电图的模型与算法

介绍一个基于单细胞动作电位计算心电图的算法,它以立体角分析为工具,计算一个与相邻细胞相连的心肌细胞对场点电势的贡献。该算法在我们用扩展细胞自动机系统Cellular3.0实现的全心脏电生理模型CardioAuto中得到实现与应用,仿真结果十分理想,并揭示细胞间的跨膜电位坡度是产生心电图波形的本质原因。对一个导联点,当细胞群的跨膜电位呈近端高远端低的坡度,心电图产生下降曲折;当细胞群呈相反的跨膜电位坡度,心电图产生上升曲折;当细胞群不存在跨膜电位坡度,心电图为基线。     

Comparison of Methods for the Extraction of DNA from Formalin-Fixed,Paraffin-Embedded Archival Tissues

Aim: Discussing a protocol involving xylene-ethanol deparaffinization on slides followed by a kit-based extraction that allows for the extraction of high quality DNA from FFPE tissues.Methods: DNA was extracted from the FFPE tissues of 16 randomly selected blocks. Methods involving deparaffinization on slides or tubes, enzyme digestion overnight or for 72 hours and isolation using phenol chloroform method or a silica-based commercial kit were compared in terms of yields, concentrations and the amplifiability.Results: The highest yield of DNA was produced from the samples that were deparaffinized on slides, digested for 72 hours and isolated with a commercial kit. Samples isolated with the phenol-chloroform method produced DNA of lower purity than the samples that were purified with kit. The samples isolated with the commercial kit resulted in better PCR amplification.Conclusion: Silica-based commercial kits and deparaffinized on slides should be considered for DNA extraction from FFPE.     

基于奇异值降维的胎心电盲分离方法

目的：胎心电是监护胎儿健康,降低围产期胎儿发病率和死亡率的重要手段。临床中多用间接法从孕妇的腹心电中获取胎心电。由于胎心电幅值较小,常被母心电和噪声掩盖,所以从孕妇的腹心电中分离出胎心电仍是诊断学难题。本文提出基于奇异值降维的胎心电盲分离方法从孕妇的心电图中有效分离出胎儿心电图。方法：临床中获取的孕妇心电信号数目较多,常为12、15、18导联。从获取的孕妇心电信号中,选取全部的腹心电和一路胸心电进行处理。如果信号中存在基线漂移,先用高通滤波器去除基线漂移,然后设置适当参数对信号进行奇异值分解降维,以便在充分保证信号信息量的前提下,降低盲分离的复杂度,减少信号的相关度,最后对降维后的信号进行盲分离处理。结果：用本文提出的方法对DaiSy数据库中的孕妇心电数据进行处理,结果表明,本文方法能有效的从孕妇心电信号中分离出胎心电。结论：从孕妇腹心电中分离出胎心电,进而对胎儿健康进行监护是一种可行的并且真正对胎儿无损的监护方法。本文提出的在盲分离前先进行奇异值降维的方法可降低盲分离的复杂度并提高分离精度。     

五种破壁方法提取贵阳腐霉Pythium guiyangense总RNA效果的比较

为了探索提取贵阳腐霉Pythium guiyangense菌株总RNA简便而有效的方法,我们分别采用石英沙研磨、超声波破碎、液氮研磨、液氮加石英沙研磨和溶菌酶消化5种方法破碎真菌细胞壁,再用市售RNAsimple total RNA kit试剂盒提取总RNA,最后通过对提取物进行琼脂糖凝胶电泳定性、紫外比色定量及PCR...     

单通道脑电信号中诱发电位的单次提取

我们正在构建一个脑控拼写装置，直接利用脑信号与计算机进行交互。在这种实时通信模式中，作为通信载体的特征脑信号淹没在自发的脑电背景中，不能采用常规的叠加平均方法来提取，而必须实现特征信号的单次识别。为使这种技术走出实验室，记录脑电的导联数应越少越好。我们采用独特的“模拟自然阅读”诱发模式，让被试从平滑移过小视窗的刺激符号串中识别靶刺激符号，以产生视觉诱发电位（VEP）。利用支持向量机方法，对三名被试位于导联Pz处EEG信号中的VEP进行了单次提取，正确识别率分别为92．1％、94．1％和91．5％，达到了较为满意的效果，为系统的实现打下了基础。     

A Low Computational Cost Algorithm for REM Sleep Detection Using Single Channel EEG

The push towards low-power and wearable sleep systems requires using minimum number of recording channels to enhance battery life, keep processing load small and be more comfortable for the user. Since most sleep stages can be identified using EEG traces, enormous power savings could be achieved by using a single channel of EEG. However, detection of REM sleep from one channel EEG is challenging due to its electroencephalographic similarities with N1 and Wake stages. In this paper we investigate a novel feature in sleep EEG that demonstrates high discriminatory ability for detecting REM phases. We then use this feature, that is based on spectral edge frequency (SEF) in the 8–16 Hz frequency band, together with the absolute power and the relative power of the signal, to develop a simple REM detection algorithm. We evaluate the performance of this proposed algorithm with overnight single channel EEG recordings of 5 training and 15 independent test subjects. Our algorithm achieved sensitivity of 83%, specificity of 89% and selectivity of 61% on a test database consisting of 2221 REM epochs. It also achieved sensitivity and selectivity of 81 and 75% on PhysioNet Sleep-EDF database consisting of 8 subjects. These results demonstrate that SEF can be a useful feature for automatic detection of REM stages of sleep from a single channel EEG.