Simulation Studies of Latency Measures of Components of the Event-Related Brain Potential

We compared the accuracy of P300 latency estimates obtained with different procedures under several simulated signal and noise conditions. Both preparatory and signal detection techniques were used. Preparatory techniques included frequency filters and spatial filters (single electrode selection and Vector filter). Signal detection techniques included peak-picking, cross-correlation, and Woody filter. Accuracy in the latency estimation increased exponentially as a function of the signal-to-noise ratio. Both Woody filter and cross-correlation provided better estimates than peak-picking, although this advantage was reduced by frequency filtering. For all signal detection techniques, Vector filter provided better estimates than single electrode selection. Large component overlap impaired the accuracy of the estimates obtained with both single electrode selection and Vector filter, but with Vector filter impairment occurred only when the overlapping component had a scalp distribution that was similar to the scalp distribution of the signal component. The effects of varying noise characteristics, P300 duration and latency, and the parameters of Vector filter were also investigated.     

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.     

Application of optimum linear filter theory to the detection of cortical signals preceding facial movement in cat

Summary Linear filter analysis was used to detect the occurrence of neuroelectric signals in associated noisy background electrical activity by matching a signal template against incoming neuroelectric data. Bach signal to be detected in the neuroelectric data consisted of a gross potential change recorded at the coronal — precruciate cortex of a cat, evoked by an auditory conditional stimulus, and related to the production of a conditioned facial movement. Detection of the occurrence of the signal corresponded closely to detection of the ensuing movement.The operation of the matched filter on the signals in noise was studied for different threshold levels of detection. Threshold settings were selected to maximize successful detections and to minimize false alarms. The results of our experimental detections agreed closely with predicted, theoretical detection levels derived from Wiener's models of optimum detection of signals in additive noise. Levels of detection were found to depend upon the signal to noise ratios and frequency spectra of the analyzed data and could be predicted a priori from a knowledge of the latter on the basis of Wiener's theory. The ability to predict optimum detection levels, by the linear filter method, of cortical electrical signals related to the production of movements may provide a basis for evaluating the merit of such signals in the design of prosthetic devices for motor control.Visiting scientist from Department of Electrical Engineering, Aria Mehr University, Tehran, Iran.     

A novel approach to track fetal movement using multi-sensor magnetocardiographic recordings

Changes in fetal magnetocardiographic (fMCG) signals are indicators for fetal body movement. We propose a novel approach to reliably extract fetal body movements based on the field strength of the fMCG signal independent of its frequency. After attenuating the maternal MCG, we use a Hilbert transform approach to identify the R-wave. At each R-wave, we compute the center-of-gravity (cog) of the coordinate positions of MCG sensors, each weighted by the magnitude of the R-wave amplitude recorded at the corresponding sensor. We then define actogram as the distance between the cog computed at each R-wave and the average of the cog from all the R-waves in a 3-min duration. By applying a linear de-trending approach to the actogram we identify the fetal body movement and compare this with the synchronous occurrence of the acceleration in the fetal heart rate. Finally, we apply this approach to the fMCG recorded simultaneously with ultrasound from a single subject and show its improved performance over the QRS-amplitude based approach in the visually verified movements. This technique could be applied to transform the detection of fetal body movement into an objective measure of fetal health and enhance the predictive value of prevalent clinical testing for fetal wellbeing.     

Microprocessor detection of electrocardiogram R-waves.

ECG monitoring devices for coronary care units require reliable R-wave dectors as a prerequisite for subsequent analysis. A simple algorithm has been developed for the detection of R-waves in an ECG signal with a microprocessor (Motorola MC6800). Detection criteria used were gradient and duration of a single upslope or downslope. Rejection of muscle noise and other artifiacts was achieved by setting system constants to optimum values which were determined by a short preliminary trial. This constant threshold system has been shown to cope with sudden changes of QRS complex amplitude better than some self adjusting systems. Assessment runs using patient tapes from a coronary care unit show total errors due to false positives and negatives are typically about 1% of the actual number of R-wave occurrences.     

Fetal heart rate monitoring based on independent component analysis

In this paper, an algorithm based on independent component analysis (ICA) for extracting the fetal heart rate (FHR) from maternal abdominal electrodes is presented. Three abdominal ECG channels are used to extract the FHR in three steps: first preprocessing procedures such as DC cancellation and low-pass filtering are applied to remove noise. Then the algorithm for multiple unknown source extraction (AMUSE) algorithm is fed to extract the sources from the observation signals include fetal ECG (FECG). Finally, FHR is extracted from FECG. The method is shown to be capable of completely revealing FECG R-peaks from observation leads even with a SNR=-200dB using semi-synthetic data.     

400例围产胎儿心电图检测分析及异常心电图的处理

本文应用ＦＥＣＧ－Ｄ型仪对４００例围产胎儿进行了ＦＥＣＧ的检测及分析。结果成功３６３例，成功率为９０．８％，正常ＦＥＣＧ３１６例占成功数的８７％，异常ＦＥＣＧ４７例占成功数的１３％。在对异常ＦＥＣＧ的类型及原因的分析中发现，异常ＦＥＣＧ中以ＦＳＴ下移＞５ｕｖ为多占５３．２％，其次为胎儿心动过速２５．６％，胎儿心动过缓１０．６％。胎儿心律不齐６．３％，ＦＱＲＳ时限增宽４．３％。胎儿心电图异常的原因为胎儿窘迫占７８．７％，脐带绕颈１２．８％，提示胎儿宫内缺氧是引起围产ＦＥＣＧ异常的主要原因，２２例（４７％）异常ＦＥＣＧ分别经胎盘给药、吸氧、输液等处理转为正常。     

胎儿心电监护系统的改进与提高

作者介绍一种先进的胎儿心电监护系统，对传统的胎儿心电监护系统作了改进和提高。该系统在胎儿心电的提取与处理方法上，解决了单纯用匹配滤波法难以提取到微弱的胎儿心电信号的问题。在系统的硬件方面，优化了信号放大器，重新设计和组建一套以４８０ＰＣ机为核心的胎儿监护系统。在软件上，采用了基于Ｗｉｎｄｏｗｓ平台，Ｂｏｒｌａｎｄ Ｃ＋＋的面向对象的软件编程。     

两导心电信号独立分量分析算法的研究

根据独立分量分析(ICA)理论,要想在观测信号中提取出独立分量,观测信号的数目必须大于或等于独立分量的数目,因此要求采用ICA算法的胎儿心电图机导联数必须大于一定数目,但在实际应用中常常难以满足这个条件。故本文提出了一种基于少数导联心电(ECG)信号的胎儿心电(FECG)提取算法,结合FECG和自适应噪声抵消算法,从两导采集于孕妇腹部体表的ECG信号中提取FECG。实验表明,该方法能够获得清晰的FECG信号。     

1519例胎儿心电图的临床价值和相关因素分析

目的对1519例胎儿全部实行常规胎儿心电图(FECG)监测工作,探讨胎儿心电图应用情况,并对异常FECG结合临床,进行临床原因分析.方法采用随机湘样方法,应用杭州产FECG-D型胎儿心电图分析仪进行监测.结果发现采用横导联方式进行监测检出率为53.5%:在孕22w进行监测检出率为45.8%;异常FECG检出率为7.64%.结论对1519例FECG的监测发现采用横导联方式并在孕22w～26w期间进行监测检出率最高、波形效果好.并对监测发现的导致FECG异常改变的临床原因与胎儿宫内窘迫的关系进行浅要分析.     

新一代胎儿监护仪的研制

胎儿心电监护是预防围产期胎死的一项重要措施，作者介绍了一种基于８０９８单片机系统，利用匹配滤波技术进行信号处理以及采用大屏幕液晶显示的新一代胎儿心电心率监护仪，它具有体积小，重量轻，耗电量低，检测算法简单可靠的特点，经临床实验，成功地检出了胎儿心电波及胎儿心率。     

基于RLS-ANC自适应滤波的FECG信号提取方法

本文应用RLS-ANC(recursive least squares adaptive noise canceⅡation)自适应滤波方法提取胎儿心电(FECG)信号.该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号.实验结果表明,本方法适应非平稳信号的能力强,收敛速度快,提取效果好于NLMS(normalized least mean squares)算法.     

基于神经网络的波型检测方法

介绍了一种基于神经网络白化匹配滤波器的ＱＲＳ波检测方法。我们用神经网络白化匹配滤波器来处理ＥＣＧ信号的低频成分,模拟其非线性及非稳态的特性。处理后的信号中含有ＥＣＧ中大部分高频成分,让其通过一线性匹配滤波器来检测ＱＲＳ波及其位置。对于大噪声的ＥＣＧ信号,在匹配滤波器后加差分滤波,取平方及滑动平均等处理,提高检测正确率。使用这种方法我们对ＭＩＴ／ＢＩＨ心电信号数据库中噪声比较大的１０５号数据进行的处     

Ultra-low-noise instrumentation and computer-based data acquisition and processing system for obtaining a noninvasive and intrauterine catheter fetal electrocardiogram

A fetal electrocardiogram (FECG) from the abdominal surface will be from 20 microVpp to unmeasurable. The intrauterine catheter signal will be from 50 microVpp to unmeasureable and will be corrupted with a significant direct current component of 10 to 200 microV. For electrophysiological information to be obtained from the abdominal and intrauterine catheter signals, the signals must be in a 0.05 to 100.0 Hz bandwidth. Because typical adult electrocardiogram systems have a noise specification of 5 to 10 microVpp from instrumentation, these systems are unacceptable for obtaining a FECG via the intrauterine catheter or noninvasively. Therefore, custom instrumentation has been developed with a noise specification of approximately 1.5 microVpp in a 0.05 to 100.0 Hz bandwidth. Design details of the custom instrumentation will be presented along with a laptop computer based data acquisition and signal processing system using LabVIEW. In addition, clinical data from the intrauterine catheter and noninvasive abdominal wall are presented to determine the feasibility of obtaining a FECG via the custom instrumentation. Clinical data obtained and documented indicates that after maternal electrocardiogram cancellation, a FECG with a good signal-to-noise ratio can be obtained in a diagnostic bandwidth of 0.05 to 100.0 Hz. Because the diagnostic bandwidth is preserved, electrophysiological information can be determined along with heart rate.     

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。     

基于混合小波变换的瞬态信号检测方法

探讨了信号的小波变换与匹配滤波的关系,指出小波变换（WT）实际上就是可变检测模板的匹配滤液过程。根据这一思想,提出了基于混合小波的信号检测方法。本文中,“混合小波变换”是指在小波分解和重构中分别使用不同的基本小波。其中分解小波用于实现可变模板的信号检测,重构小波则用以增强被检测信号的特征。我们用该方法对实测脑电信号（EEG）中瞬态脉冲干扰进行检测。实验结果表明该方法能有效地检测出EEG中的瞬态脉冲。     

基于DSP的自适应胎儿心电图仪研究

介绍了一种自适应胎儿心电图仪的研究与实现方法.研制了TMS320LF2407A数字信号处理芯片为核心的嵌入式系统.根据自适应噪声抵消(Adaptive noise cancelling,ANC)原理,研究与实现了自适应噪声抵消软件算法,无创无损提取胎儿心电信号,并通过通讯接口送至上位机实时显示,分析和记录处理结果.实验表明该仪器设计合理,性能良好.     

Extraction of short-latency evoked potentials using a combination of wavelets and evolutionary algorithms

Somatosensory evoked potentials, recorded at the spine or scalp of a patient, are contaminated by noise. It is common practice to use ensemble averaging to remove the noise, which usually requires a large number of responses to produce one averaged signal. In this paper a post-processing technique is shown which uses a combination of wavelets and evolutionary algorithms to produce a representative waveform with fewer responses. The most suitable wavelets and a set of weights are selected by an evolutionary algorithm to form a filter bank, which enhances the extraction of evoked potentials from noisy recordings.     

基于非负盲分离的胎儿心率检测方法

胎儿心率监测是一种有效评估胎儿当前健康状况的重要参考依据。为了可以快速准确地获取胎儿心率,该文提出一种基于非负盲分离的胎儿心率检测方法。该方法首先对采集得到的腹壁信号进行预处理,平稳小波变换后重构出母亲心电信号;接着,采用相减法去除母亲心电信号,再把剩下含有噪声的胎儿心电信号通过时频变换得到Born-Jordan分布;最后,利用非负矩阵分解得到胎儿心电的特征信号,检测其R波位置求得胎儿瞬时心率。实验结果表明,该方法可以快速、准确有效地获得胎儿地瞬时心率数据。     

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.