基于移动智能终端的单通道胎儿心电监护系统

目的 设计基于移动智能终端的单通道胎儿心电监护系统,以实现扩展卡尔曼滤波(extended Kalman filtering,EKF)和奇异值分解(singular value decomposition,SVD)相结合的单通道胎儿心电提取算法,实时获取高信噪比的胎儿心电信号,完成胎心监护的远程移动医疗.方法 利用STM32单片机控制24位采样芯片ADS1298,对单通道的孕妇腹部信号进行采集,并将采集后的数据经蓝牙传送给移动智能终端,在基于Android的移动智能终端上实现EKF和SVD相结合的单通道胎儿心电提取算法,完成对胎儿心电的实时提取、显示、存储与分析,计算心律变异率,实现对整个监护系统进行控制等功能.结果 测试结果表明,该系统可从单通道孕妇腹部信号中准确提取出胎儿心电信号,准确度为95.60％,阳性预测率为98.71％,系统工作稳定,连续处理5个胎心周期的数据用时约为70μs,小于一个母体心动周期(约0.8 s)的时间,适于临床对胎儿心电的实时监护.结论 该系统实时性强、准确率高、工作稳定、操作简单、便于携带,实现了对胎心监护的可穿戴式远程移动医疗,适合社区医院和家庭使用.     

一种改进的FastICA算法在胎儿心电提取中的应用

目的为了改进传统FastICA算法对初始权值较敏感的问题,本文提出一种基于超松弛因子改进的FastICA算法来提取胎儿心电。方法首先对Da ISy数据库中的母体腹部混合信号进行中心化和白化处理,去除信号间的相关性;然后在牛顿迭代算法中引入超松弛因子对随机产生的初始权值进行处理,再用改进FastICA算法提取胎儿心电;最后对胎儿心电信号的提取结果通过可视化的波形和量化指标进行评估。结果实验结果显示该算法平均迭代次数由改进前的55次降到15次,信噪比也得到提高,并且改进后算法提取出来的胎儿心电几乎不掺杂母体心电。结论基于超松弛因子改进的FastICA算法,在保持收敛速度的同时,放宽了对初始权值的要求,避免了收敛不平衡,减少了迭代次数,可以提取出比较清晰的胎儿心电。     

一种基于单通道腹部信号的胎儿心电提取算法

设计一种基于单通道孕腹部信号的胎儿心电提取算法,分别提取出母亲心电和胎儿心电,并计算出母亲心率和胎儿心率。首先对单通道孕腹部信号进行k-TEO(k=19)变换,突出母亲心电的QRS波,从而通过简单的阈值法确定母亲心电的R波位置,接着通过在相邻R波间重采样以获得相同的R-R间期T,这样经过一个间隔为T的梳状滤波器就可以分离出相同R-R间期的母亲心电,然后再一次在相邻R波间进行重采样恢复原来的R-R间期就可以获得实际的母亲心电了。原始腹部信号减去上面提取的母亲心电后,胎儿心电QRS波的信噪比大大提高,通过再次应用提取母亲心电的算法即可得到“干净”的胎儿心电波形。选取Physionet数据库中的8 组(26 通道)孕腹部信号数据进行分析,计算每个通道数据的胎儿心电QRS波位置识别灵敏度、阳性检测率和准确性。结果表明,胎儿心电QRS波的识别准确率达到87.1%,其中有6 个通道达到100%。另外计算每个通道的母亲心率和胎儿心率并做统计分析,发现每一组中各个通道的母亲平均心率和胎儿平均心率都非常接近,同一组中各通道间母亲平均心率最大误差为0.1次/min, 而胎儿平均心率最大误差也只有0.9次/min,进一步证明算法的可靠性。     

结合快速独立成分分析算法和卷积神经网络的胎儿心电信号提取与分析方法

胎儿心电信号为胎儿异常情况的早期诊断和干预提供了重要的临床信息，本文提出一种胎儿心电信号提取与分析的新方法。首先，将改进的快速独立成分分析（FastICA）法和奇异值分解（SVD）算法结合，来提取高质量胎儿心电信号并解决波形缺失问题。其次，运用一种新的卷积神经网络（CNN）模型识别胎儿心电信号QRS复合波，并有效解决波形重叠问题。最终，实现胎儿心电信号的高质量提取与胎儿QRS复合波的智能识别。以复杂生理信号研究资源网2013年心脏病学计算挑战赛（PhysioNet2013）数据库资料对本文所提方法进行验证，结果表明该提取算法平均灵敏度与阳性预测值为98.21%和99.52%;QRS复合波识别算法平均灵敏度与阳性预测值为94.14%和95.80%，相较于其他研究成果均有较好的提升。综上，本文提出的算法与模型具有一定的实践意义，今后或可为临床医学决策提供理论依据。     

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。     

基于平稳小波变换的胎儿心电提取方法

胎儿心电信号的提取对孕期胎儿健康状况的检测具有重要意义。本文提出一种基于平稳小波变换的单/多通道胎儿心电提取方法。多通道环境下输入信号包括腹部混合信号和母体心电信号,单通道环境下母体心电信号采用对腹部混合信号进行窗口平均法获得,然后对信号进行平稳小波变换与阈值去噪,继而提取胎儿心电信号。Physio Net数据测试实验表明,该方法在单/多通道的环境下均能成功提取到清晰的胎儿心电信号,并且能有效地消除噪声。     

基于经验模态分解自适应滤波的胎儿心电信号提取

目的提出了一种基于经验模态分解自适应滤波的胎儿心电信号提取法。方法首先利用经验模态分解算法对孕妇腹部信号进行分解得到一组内模函数(IMF),然后将这组IMF作为自适应滤波器的主输入信号,并将孕妇胸部信号作为参考输入信号。通过学习算法自适应组合IMF,滤除母体心电信号成分,从而提取胎儿心电信号。结果与结论基于仿真和临床的实验结果表明,该方法提取的胎儿心电信号误差小,性能优于传统的最小均方和归一化最小均方自适应滤波算法。     

基于BP神经网络的胎儿心电提取算法研究

目的针对胎儿心电不易提取的问题,提出一种从孕妇腹部混合心电信号和胸部心电信号中提取胎儿心电的方法。方法采用反向传播(BP)神经网络预测孕妇腹部混合心电信号中母体心电的真实形态,从腹部混合信号中减去预测的母体心电信号便得到胎儿心电信号。与小波阈值去燥算法和自适应滤波算法比较,评价BP神经网络算法可行性。结果相比小波阈值去燥算法和自适应滤波算法,该算法准确度为94.12%,灵敏度为96.97%。这两项指标均优于小波阈值去燥算法的80.52%、93.94%和自适应滤波算法的87.88%、87.88%。结论基于BP神经网络的方法可以提取到纯净的胎儿心电信号,对于胎儿心电监护有一定的应用价值。     

基于分块扩展Infomax算法的胎儿心电分离

背景：胎儿心电是围产期对胎儿发育状况监测的重要参数,准确将胎儿心电从母体心电中分离出来是目前研究的重点。 目的：采用分块扩展Infomax算法能够快速、有效的将胎儿心电分离出来。 方法：利用分块扩展Infomax算法结合四阶统计去相关学习规则,使信号加权协方差阵的非对角元素最小化,改善算法收敛速度。 结果与结论：扩展Infomax能实现超高斯和亚高斯信号的同时分离,文章提出的算法在收敛速度上要快于扩展Infomax的收敛速度。扩展Infomax算法能对胎儿心电实现有效的提取,由于分块扩展Infomax算法每次处理的数据量远少于扩展Infomax算法处理的数据量,且分块Infomax算法的迭代次数也远小于扩展Infomax的迭代次数,从而实现了胎儿心电的快速分离。     

空间滤波法用于胎儿心电信号的提取

本文综述了将空间滤波法用于胎儿心电信号的提取技术,重点介绍了奇异值分解法(Singular Value Decomposition,SVD)的应用。主要内容包括:利用正交基函数消去母亲心电的方法;用SVD两步法消去母亲心电的方法;基于“逆问题”原理的SVD方法。     

Real-time abdominal fetal ECG recording using a hardware correlator.

A real-time fetal ECG monitoring system using abdominal recording is presented. The system is based on an IBM AT compatible personal computer. The computer lacks the performance required for real-time analysis. Therefore, a new design of a fast hardware correlator board was developed to enhance the computer throughput. The technique is based on a cross-correlation procedure. An averaged maternal ECG waveform is derived using the cross-correlation function for the waveform's alignment. With this procedure a template signal corresponding to one complete maternal ECG is obtained. The averaged maternal ECG is then subtracted from the abdominal signals. Thus, it is possible to detect all the fetal ORS complexes in spite of their coincidence with the maternal ECG. An average fetal ECG is then extracted to improve the signal-to-noise ratio, making it possible to recognize fetal P and T waves.     

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.     

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.     

Research of fetal ECG extraction using wavelet analysis and adaptive filtering

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.     

Real-time multichannel abdominal fetal ECG monitor using Digital Signal CoProcessor

A real-time multichannel fetal ECG monitor based on a personal computer (PC) and a MOTOROLA DSP56001 Digital Signal CoProcessor (DSP) is introduced. The DSP board is plugged into the PC, which functions as a HOST computer. An analog 8 Leads Interface and Analog to Digital circuits module is connected to the DSP through a synchronous, opticalisolated communication channel.

The fetal ECG detection is based on a cross-correlation technique. An averaged maternal ECG waveform is generated using a cross-correlation alignment procedure and a user-defined template. The fetal ECG signals present in the maternal waveform is suppressed during the averaging procedure, since both are uncorrelated. The average maternal ECG waveform is then subtracted from the abdominal real time signals, and maternal-free fetal ECGs signals are obtained, including fetal QRS complexes that coincide with maternal ones. Using the abdominal ECGs signals after subtraction, an averaged fetal waveform is generated. The maternal and the fetal heart rate are calculated during the process.

The algorithm described above can be performed in real time on up to eight abdominal ECG traces by the DSP, and the desired results are passed to the HOST PC, to be stored and displayed. Electrodes positioning procedures for detecting the fetal QRS complexes with the best signal to noise ratio are not needed. Using the multichannel system, the user can select the best channel for fetal QRS detection, and accurate results for the heart rate signal are obtained. Averaged fetal waveforms are obtained from all the leads.     

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。     

A Comparison of Single Channel Fetal ECG Extraction Methods

The abdominal electrocardiogram (ECG) provides a non-invasive method for monitoring the fetal cardiac activity in pregnant women. However, the temporal and frequency overlap between the fetal ECG (FECG), the maternal ECG (MECG) and noise results in a challenging source separation problem. This work seeks to compare temporal extraction methods for extracting the fetal signal and estimating fetal heart rate. A novel method for MECG cancelation using an echo state neural network (ESN) based filtering approach was compared with the least mean square (LMS), the recursive least square (RLS) adaptive filter and template subtraction (TS) techniques. Analysis was performed using real signals from two databases composing a total of 4 h 22 min of data from nine pregnant women with 37,452 reference fetal beats. The effects of preprocessing the signals was empirically evaluated. The results demonstrate that the ESN based algorithm performs best on the test data with an F1 measure of 90.2% as compared to the LMS (87.9%), RLS (88.2%) and the TS (89.3%) techniques. Results suggest that a higher baseline wander high pass cut-off frequency than traditionally used for FECG analysis significantly increases performance for all evaluated methods. Open source code for the benchmark methods are made available to allow comparison and reproducibility on the public domain data.     

Computerized detection of low-level fetal signals in the maternal abdominal electrocardiogram

This paper describes a method for detecting low-level fetal ECG signals in maternal abdominal ECG recordings. Detection is based on a systematic application of the principle that the fetal ECG contains proportionately greater high-frequency components than does the maternal ECG. Adaptive subtraction of the maternal high-frequency components is used to detect the fetal R-waves. The method is found to detect the fetal ECG even in many cases where the maternal and fetal R-waves coincide or occur in close proximity to each other. Recursive time-coherent averaging is then used to significantly improve the signal-to-noise ratio of the fetal ECG to the point where the fetal P and T waves may be observed.     

Single-lead fetal electrocardiogram estimation by means of combining R-peak detection,resampling and comb filter

Minimal detecting electrodes are preferred to miniaturise fetal electrocardiogram (fECG) monitoring devices for application in non-clinical environments. In this paper, a new method to estimate the fECG using a single-lead abdominal signal is introduced. In this method, for a preprocessed abdominal ECG recording, we follow a multi-step procedure to estimate the fECG signal. First, the locations of the maternal R-peaks are detected. Each R–R interval in the abdominal signal is resampled to have the same number of samples by changing its corresponding sampling frequency. A comb filter, which has teeth that coincide with the harmonics of the maternal electrocardiogram (mECG), is applied to the resampled signal. Each R–R interval in the filtered signal is resampled again to recover its original sampling frequency, and the mECG signal is obtained. This mECG signal is subtracted from the abdominal signal, and the residual signal is considered to be a primary estimate of the fECG signal. The same procedure can be applied to the residual signal to enhance the fECG signal. Compared to two other single-lead-based methods, singular value decomposition and nonlinear state-space projection, the proposed method has shown improved robustness and fidelity in restoration of the fECG during testing with synthetic ECG signals and a real fetal ECG database from MIT-BIH PhysioBank.