基于经验模式分解与独立分量分析的心电信号消噪方法

针对小波独立分量分析法(W-ICA)在心电信号消噪中小波变换缺乏自适应性，且较难选取最优小波基的问题，提出了一种将经验模式分解与独立分量分析相结合的小波独立分量分析法。该方法结合经验模式分解与独立分量分析各自的优点，利用经验模式分解对心电信号进行自适应分解，然后应用独立分量分析法对选取的本征模态函数进行分离，将分离后的分量进行两层重构，从而得消噪后的心电信号。通过利用MIT-BIH心率失常数据库中的数据进行仿真实验，结果表明该方法可以较好地消除心电信号中的噪声，消噪后信号与原信号的相关系数可达0.96。     

基于图像序列的脉象分析*◆

背景：根据目前报道来看，用于脉搏信号检测的传感器多为单点或复合多点压力传感器，而单点脉搏信号或简单几个点的脉搏信号不能细致反映切脉皮肤表面空间各点的变化，无法全面描述脉象信息，取得的生理信息比较少。 目的：对脉象的二维脉象图进行图像空间域和频率域的分析及特征提取，运用仿生手进行脉象特征提取，并对试验结果进行对比分析。 方法：应用自制的基于单目CCD摄像头的脉搏图像传感器，采集仿生手(中医脉象模拟系统)桡动脉的动态脉搏图像序列，结合透镜成像原理，根据各帧图像中网格面积的变化获取脉搏图像表面多点的三维离面位移变化量。在脉长方向选取8个网格点的数据和时间轴方向选取441帧构成二维矩阵后将其转化为二维灰度图像。对各脉象的二维脉象图进行图像空间域和频率域的分析及特征提取。 结果与结论：应用该方法可较直观地获取脉搏图像表面多点离面位移变化信息，并可根据提取的特征量进行脉象的分析及分类。     

皮质脑电图小波分析定位运动区的探索性研究

目的研究术中皮质脑电图(ECoG)小波分析对运动区皮质定位的可行性。方法利用小波变换,对ECoG信号进行多层分解和重构,提取4个主要频带(δ、θ、μ和β)重构信号的运动前后能量比(ERD)为特征量,并构造特定阈值进行分类,然后与相应手指弯曲运动数据进行对照比较,分析检测的正确率。结果d6子频带(μ频带)的运动前后ERD变化最明显;以40%为阈值进行分类,其定位运动区的正确率达到93%。结论通过小波分析对ECoG的特征进行提取和分类,可有效定位运动区皮质。     

脑电图功率谱和近似熵在人脑发育过程的研究

目的：研究人脑发育过程中脑电功率值和近熵的变化特征。方法：在一组共２０９例不同年龄组建康人中进行定量脑电图功率谱和近似熵计算。结果脑电图功率谱方面总的来说δ频率,θ频率等慢活动减少,α,β频率增加。近似熵随年龄增大表现为进行性增大,各年龄组以上各指标的差异用方差检测有显著性意义。     

电阻抗胃动力信息检测与处理方法****☆

目的：从电-机复合系统的认识高度，研究采用生物电阻抗技术的无创胃动力测量和评价方法，研制专用实验检测系统。 方法：采用生物电阻抗技术从体表进行无创胃动力检测，并与同步胃电相结合，研究食物消化过程中的电－机特性，包括节律、传导、胃排空过程以及其影响因素；运用小波变换分离、提取胃阻抗信号；通过能谱和频谱分析方法，根据主能量和支配频率将信号分类; 设置并计算胃动力参数，包括节律、频率谱、功率谱、动态谱、主频变异系数FIC、主频下功率变异系数PIC等。 结果：建立了生物电阻抗无创胃动力测量和评价方法，研制了专用实验检测系统。28例受试者在不同时段的胃动力对照实验结果表明，胃动力参数2~4 cpm收缩节律比、2~4 cpm功率比、主频变异系数FIC在下午时段比上午明显增加，但主频下功率变异系数PIC没有显著性变化；餐前、餐后胃动力对照实验结果表明，空腹时胃运动的节律比较紊乱，进食后节律性增加；药物对胃动力影响实验表明铝碳酸镁片的作用在于中和胃酸，多潘立酮片有促进胃动力的功效。 结论：采用无创生物阻抗方法和同步胃电测量相结合的技术，可研究复杂的胃动力电－机过程，包括节律性、传导性、胃排空过程以及影响因素，可为胃动力测量和评价提供一种新的、有效的无创方法。     

两种高压静电场对大鼠血细胞及血清生物化学指标的影响★

目的：从电-机复合系统的认识高度，研究采用生物电阻抗技术的无创胃动力测量和评价方法，研制专用实验检测系统。 方法：采用生物电阻抗技术从体表进行无创胃动力检测，并与同步胃电相结合，研究食物消化过程中的电－机特性，包括节律、传导、胃排空过程以及其影响因素；运用小波变换分离、提取胃阻抗信号；通过能谱和频谱分析方法，根据主能量和支配频率将信号分类; 设置并计算胃动力参数，包括节律、频率谱、功率谱、动态谱、主频变异系数FIC、主频下功率变异系数PIC等。 结果：建立了生物电阻抗无创胃动力测量和评价方法，研制了专用实验检测系统。28例受试者在不同时段的胃动力对照实验结果表明，胃动力参数2~4 cpm收缩节律比、2~4 cpm功率比、主频变异系数FIC在下午时段比上午明显增加，但主频下功率变异系数PIC没有显著性变化；餐前、餐后胃动力对照实验结果表明，空腹时胃运动的节律比较紊乱，进食后节律性增加；药物对胃动力影响实验表明铝碳酸镁片的作用在于中和胃酸，多潘立酮片有促进胃动力的功效。 结论：采用无创生物阻抗方法和同步胃电测量相结合的技术，可研究复杂的胃动力电－机过程，包括节律性、传导性、胃排空过程以及影响因素，可为胃动力测量和评价提供一种新的、有效的无创方法。     

基于B-样条双正交小波R波的标定和QRS波检测

实现心电信号QRS波检测的算法很多，文章给出了一种基于B-样条双正交小波对心电信号R波峰值标定和QRS波波段检测的方法。利用双正交样条小波等效滤波器，对心电信号按Mallat算法进行快速变换；从信号奇异点的李氏指数与模极大值关系的角度，分析心电信号奇异点（R峰值点）与其小波变换模极大值对的零交叉点的关系。用二次B-样条小波滤波器组对心电数字信号进行4个尺度的小波分解，然后根据分解的尺度波形特性求出正负极值对过零点，即R波峰值，并检测出QRS波段。采用Matlab编程实现该算法。从实验结果可以得出，该算法对心电信号中QRS波群的特征提取和几种常见的心电干扰具有较强的鲁棒性，经MIT-BIH标准心律失常数据库验证，QRS波的正确检测率达99.9%。文中给出了程序流程图。     

脉搏波形采集与辅助诊断系统的设计

在分析现有脉搏信号测量技术及其处理方法的基础上，设计了以AT89S52单片机为下位机，PC机为上位机的脉搏信号采集装置。利用Delphi强大的数据库支持特性和Delphi与Matlab混合编程技术，开发了脉搏信号辅助诊断系统。该系统界面友好，操作简单，数据处理能力强，实现了脉搏信号的采集，脉搏波形的实时显示、回放等功能。通过对临床患者脉搏数据的实际采集和处理，验证了该系统的有效性和实用性。     

监测脑电α波的无线传感器网络系统

背景：临床上一般采用多导睡眠记录仪监测脑电α波来研究失眠等问题。多导睡眠记录仪存在两个问题：①众多长导线严重束缚被试者，导致其不能正常睡眠，影响了身心状态，无法获得准确结果。②由于脑电信号是极其微弱的电生理信号，所以非常容易被交流电干扰，使得分析软件无法识别有用信号。因此，需要设计一种能够克服上述缺点的监测系统。 目的：设计一种监测脑电信号的无线传感器网络系统，不影响被试者身心状态，获得不受干扰的、准确的脑电信号。 设计：采用先进行理论分析，建立电路模型，再设计实现实际应用电路。 单位：解放军第三○五医院。 材料：解放军第三○五医院提供临床实验环境。北京新兴阳升科技有限公司生产的多导睡眠记录仪做为对比实验设备。采用Matlab软件系统设计分析软件。 方法：首先于2005年在解放军第三○五医院进行理论分析，建立干扰模型和无线传感器网络系统模型。2006年根据电路模型，设计实现了能够有效抑制干扰和小体积的无线脑电传感器。实验设计的无线脑电传感器于2006-02/08在解放军第三○五医院完成实际使用观察，此次使用过程经医院伦理委员会批准，受试者为院内工作人员，对实验的目的、过程、结果完全知情同意。最后将实际使同结果与多导睡眠记录仪的记录结果进行对比。 主要观察指标：在干扰环境下获得信号的频谱。 结果：无线脑电传感器在干扰条件下获得的脑电信号在8~12 Hz的α波频率范围内的功率谱峰值为6 926.043，交流电干扰在50 Hz的频率上的功率谱为0.356。同时使用多导睡眠记录仪获得的脑电信号在8~12 Hz的α波频率范围内的功率谱峰值为1 112.3，交流电干扰在50 Hz的频率上的功率谱为85 440。 结论：系统能够获得良好的脑电α波信号，并且很好地抑制交流电干扰。     

基于灰度对比和自适应小波变换的X射线图像增强

背景：X射线检查作为常规的检查方式得到了广泛的应用,然而由于现有技术的局限性,使得X射线图像往往具有灰度对比度低和噪声影响等缺点,因此,现有的X射线图像往往达不到医生的要求。 目的：增强和去噪处理对比度较低且含有噪声的X射线图像,以达到易于医生理解和识别的目的。 方法：针对空间域处理和变换域处理增强X射线图像的不足,提出了基于灰度对比和自适应小波变换的X射线图像增强算法。首先,选择需要增强和减弱的灰度范围,并根据八邻域灰度对比增强算法对X射线图像进行灰度变换,并用中值滤波算法对图像进行平滑;然后,对X射线图像进行小波分解,并运用相邻分解层之间相关系数的大小来确定细节信号和噪声。 结果与结论：应用了灰度对比和自适应小波变换相结合的X射线图像增强算法,把基于空间域增强的方法和基于变换域的方法有机地结合起来,比传统的单一增强方法更为优越。实验结果证明它能自适应地增强X射线图像的灰度对比,使得图像细节的显示更加清晰,同时在一定程度上去除了噪声的干扰,对于灰度对比度较低的图像效果更加明显。     

Time-frequency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform

A single pulse of Transcranial Magnetic Stimulation (TMS) generates electroencephalogram (EEG) oscillations that are thought to reflect intrinsic properties of the stimulated cortical area and its fast interactions with other cortical areas. Thus, a tool to decompose TMS-evoked oscillations in the time-frequency domain on a millisecond timescale and on a broadband frequency range may help to understand information transfer across cortical oscillators. Some recent studies have employed algorithms based on the Wavelet Transform (WT) to study TMS-evoked EEG oscillations in healthy and pathological conditions. However, these methods do not allow to describe TMS-evoked EEG oscillations with high resolution in time and frequency domains simultaneously. Here, we first develop an algorithm based on Hilbert-Huang Transform (HHT) to compute statistically significant time-frequency spectra of TMS-evoked EEG oscillations on a single trial basis. Then, we compared the performances of the HHT-based algorithm with the WT-based one by applying both of them to a set of simulated signals. Finally, we applied both algorithms to real TMS-evoked potentials recorded in healthy or schizophrenic subjects. We found that the HHT-based algorithm outperforms the WT-based one in detecting the time onset of TMS-evoked oscillations in the classical EEG bands. These results suggest that the HHT-based algorithm may be used to study the communication between different cortical oscillators on a fine time scale.     

Time-frequency component analyser and its application to brain oscillatory activity

Currently, event-related potential (ERP) signals are analysed in the time domain (ERP technique) or in the frequency domain (Fourier analysis and variants). In techniques of time-domain and frequency-domain analysis (short-time Fourier transform, wavelet transform) assumptions concerning linearity, stationarity, and templates are made about the brain signals. In the time-frequency component analyser (TFCA), the assumption is that the signal has one or more components with non-overlapping supports in the time-frequency plane. In this study, the TFCA technique was applied to ERPs. TFCA determined and extracted the oscillatory components from the signal and, simultaneously, localized them in the time-frequency plane with high resolution and negligible cross-term contamination. The results obtained by means of TFCA were compared with those obtained by means of other commonly used techniques of ERP analysis, such as bilinear time-frequency distributions and wavelet analysis. It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time-frequency domain and for studying the intricate, frequency-based dynamics of the human brain.     

HHT based cardiopulmonary coupling analysis for sleep apnea detection

Study objectivesTo validate the feasibility of the Hilbert–Huang transform (HHT) based cardiopulmonary coupling (CPC) technique in respiratory events detection and estimation of the severity of apnea/hypopnea.MethodsThe HHT-CPC sleep spectrogram technique was applied to a total of 69 single-lead ECG signals downloaded from the Physionet Sleep Apnea Database. Sleep spectrograms generated by both the original and the improved CPC method were compared on the structure distribution and time–frequency resolution. The performance of respiratory events detection by using the power of low frequency coupling (pLFC) in the new method was estimated by receiver operating characteristic analysis. Furthermore, correlation between HHT-CPC index (temporal Variability of Dominant Frequency, TVDF) and conventional OSAHS scoring was computed.ResultsThe HHT-CPC spectrum provides much finer temporal resolution and frequency resolution (8 s and 0.001 Hz) compared with the original CPC (8.5 min and 0.004 Hz). The area under the ROC curve of pLFC was 0.79 in distinguishing respiratory events from normal breathing. Significant differences were found in TVDF among groups with different severities of OSAHS (normal, mild, moderate, and severe, p < 0.001). TVDF has a strong negative correlation with the apnea/hypopnea index (AHI, correlation coefficient ?0.71).ConclusionsThe HHT-CPC spectrum could exhibit more detailed temporal-frequency information about cardiopulmonary coupling during sleep. As two spectrographic markers, pLFC and TVDF can be used to identify respiratory events and represent the disruption extent of sleep architecture in patients with sleep apnea/hypopnea, respectively. The proposed technique might serve as a complementary approach to enhance diagnostic efforts.     

Delay estimation for cortico-peripheral relations

In neurophysiology, time delays between concurrently measured time series are usually estimated from the slope of a straight line fitted to the phase spectrum. We point out that this estimate is valid only in the case in which, one signal is a mere time-delayed copy of the other one. We present a procedure for delay estimation that applies to a much wider class of systems with nontrivial phase spectrum like for example lowpass filters. The procedure is based on the Hilbert transform relation between the phase of a linear system and its log gain. The Hilbert transform relation is nonlocal in frequency space, a fact that limits its applicability to experimental data. We explore these limits, and demonstrate that the method is applicable to neurophysiological time series. We present the successful application of the Hilbert transform behavior method to concurrently recorded epicortical brain activity and peripheral tremor. We point out and explain physiologically unreasonable delay estimates given by the traditional method. Finally, we discuss the assumptions underlying the applicability of the Hilbert transform method in the neuroscience context.     

A wavelet-based method for local phase extraction from a multi-frequency oscillatory signal

One of the challenges in analyzing neuronal activity is to correlate discrete signal, such as action potentials with a signal having a continuous waveform such as oscillating local field potentials (LFPs). Studies in several systems have shown that some aspects of information coding involve characteristics that intertwine both signals. An action potential is a fast transitory phenomenon that occurs at high frequencies whereas a LFP is a low frequency phenomenon. The study of correlations between these signals requires a good estimation of both instantaneous phase and instantaneous frequency. To extract the instantaneous phase, common techniques rely on the Hilbert transform performed on a filtered signal, which discards temporal information. Therefore, time-frequency methods are best fitted for non-stationary signals, since they preserve both time and frequency information. We propose a new algorithmic procedure that uses wavelet transform and ridge extraction for signals that contain one or more oscillatory frequencies and whose oscillatory frequencies may shift as a function of time. This procedure provides estimates of phase, frequency and temporal features. It can be automated, produces manageable amounts of data and allows human supervision. Because of such advantages, this method is particularly suitable for analyzing synchronization between LFPs and unitary events.     

Source localization in magnetoencephalography to identify epileptogenic foci

Rationale

Magnetoencephalography (MEG) is useful to localize epileptic foci in epilepsy as MEG has higher spatio-temporal resolution than conventional diagnostic imaging studies; positron emission computed tomography, single photon emission computed tomography and magnetic resonance imaging (MRI).

Methods

We use 204-channel helmet-shaped MEG with a sampling rate of 600 Hz. A single dipole method calculates equivalent current dipoles to localize epileptic sources. The equivalent current dipoles are superimposed onto MRI as magnetic source imaging (MSI). Ictal MEG data are analyzed using time-frequency analysis. The power spectrum density is calculated using short-time Fourier transform and superimposed onto MRI results.

Results

Clustered equivalent current dipoles represent epileptogenic zones in patients with localization-related epilepsy. The surgical plan is reliably developed from source localizations of dipoles and power spectrum of interictal spike discharges, and ictal frequency.

Conclusion

MEG is indispensable in diagnosis and surgical resection for epilepsy to accurately localize the epileptogenic zone.     

表面肌电信号的分析和特征提取***☆◆

背景：表面肌电信号的检测与分析对临床诊断人体功能状况以及患者康复具有重要意义。 目的：对表面肌电信号的采集、信号处理、特征分析和特征值提取方面进行分析。 方法：在人体屈伸肘部的过程中,选取人体上肢4块肌肉(肱三头肌,肘肌,肱二头肌,肱桡肌)分别检测表面肌电信号,对表面肌电信号进行陷波和带通滤波等预处理(优化)。在此基础上分析表面肌电信号的特征,并应用不同的特征值提取方法对优化后的表面肌电信号进行了特征提取。 结果与结论：时域方法最早应用于肌电信号分析,易提取、方法简单;频域方法提取的特征值较稳定,使得频域方法成为肌电信号处理技术的主流;以小波变换为代表的时-频分析方法因结合了时域、频域两方法的特性,在肌电信号分析方面颇有潜力。 关键词：表面肌电信号;信号采集;信号处理(优化);特征分析;特征值提取。     

High resolution study of sleep spindles.

OBJECTIVE: Universal high-resolution time-frequency parameterization of sleep EEG structures. METHODS: A new algorithm called Matching Pursuit was used for the decomposition of sleep EEG into waveforms chosen from a large and redundant set of functions. As a result all signal structures were parameterized in terms of their frequency, time occurrence, time span and energy. Slow wave activity and sleep spindles were identified according to neurophysiological criteria and various distributions describing their time evolution, topographical and frequency characteristics were constructed. RESULTS: Two types of sleep spindles of different topological and spectral properties were identified. High time-frequency resolution made possible separation of superimposed spindles. Cross-correlation between high- and low-frequency components of superimposed spindles revealed a fixed time-delay between them, the high-frequency component preceding the low-frequency one. CONCLUSION: The results of our study suggest that processes of generation of both types of sleep spindles are weakly coupled.     

Time-frequency analysis of spike-wave discharges using a modified wavelet transform

The continuous Morlet wavelet transform was used for the analysis of the time-frequency pattern of spike-wave discharges (SWD) as can be recorded in a genetic animal model of absence epilepsy (rats of the WAG/Rij strain). We developed a new wavelet transform that allows to obtain the time-frequency dynamics of the dominating rhythm during the discharges. SWD were analyzed pre- and post-administration of certain drugs. SWD recorded predrug demonstrate quite uniform time-frequency dynamics of the dominant rhythm. The beginning of the discharge has a short period with the highest frequency value (up to 15 Hz). Then the frequency decreases to 7-9 Hz and frequency modulation occurs during the discharge in this range with a period of 0.5-0.7 s. Specific changes of SWD time-frequency dynamics were found after the administration of psychoactive drugs, addressing different brain mediator and modulator systems. Short multiple SWDs appeared under low (0.5 mg/kg) doses of haloperidol, they are characterized by a fast frequency decrease to 5-6 Hz at the end of every discharge. The frequency of the dominant frequency of SWD was not stable in long lasting SWD after 1.0 mg/kg or more haloperidol: then two periodicities were found. Long lasting SWD seen after the administration of vigabatrin showed a stable frequency of the discharge. The EEG after Ketamin showed a distinct 5 s quasiperiodicity. No clear changes of time-frequency dynamics of SWD were found after perilamine. It can be concluded that the use of the modified Morlet wavelet transform allows to describe significant parameters of the dynamics in the time-frequency domain of the dominant rhythm of SWD that were not previously detected.