基于形态学的心电信号基线漂移矫正方法

基线漂移噪声是心电信号(ECG)中噪声的主要组成成分,矫正基线漂移是心电信号预处理的重要一步。目前,矫正ECG信号中基线漂移的方法包括小波变换、曲线拟合法以及FIR和IIR滤波的方法,但因计算量较大、实时性受信号长度影响和截止频率固定等因素,使得滤波带来不便。提出数学形态滤波器来矫正基线漂移,并根据心电信号的几何特征设计参数。经验证：该方法能有效矫正基线漂移,较好地保留信号特征。     

Removal of cardiopulmonary resuscitation artifacts from human ECG using an efficient matching pursuit-like algorithm

We present a computationally efficient and numerically robust solution to the problem of removing artifacts due to precordial compressions and ventilations from the human electrocardiogram (ECG) in an emergency medicine setting. Incorporated into automated external defibrillators, this would allow for simultaneous ECG signal analysis and administration of precordial compressions and ventilations, resulting in significant clinical improvement to the treatment of cardiac arrest patients. While we have previously demonstrated the feasibility of such artifact removal using a multichannel Wiener filter, we here focus on an efficient matching pursuit-like approach making practical real-time implementations of such a scheme feasible for a wide variety of sampling rates and filter lengths. Using more realistic data than what have been previously available, we present evidence showing the excellent performance of our approach and quantify its computational complexity.     

Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry

In this paper, several simple and efficient sign based normalized adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancelation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (LMS) based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio and computational complexity.     

A nonlinear adaptive method of elimination of power line interference in ECG signals

A new method of elimination of power line noise in electrocardiogram signals is presented. The proposed method employs, as its main building block, a recently developed signal processing algorithm capable of extracting a specified component of a signal and tracking its variations over time. Design considerations and performance of the proposed method are presented with the aid of computer simulations. Superior performance is observed in terms of effective elimination of noise under conditions of varying powerline interference frequency. The proposed method presents a simple and robust structure which complies with practical constraints involved in the problem such as low computational resource availability and low sampling frequency.     

Phase-rectified signal averaging used to estimate the dominant frequencies in ECG signals during atrial fibrillation

Phase-rectified signal averaging (PRSA) is a technique recently introduced to enhance quasi-periodic signal components. An important parameter that can be extracted from surface ECG is the dominant frequency (DF) of atrial fibrillation (AF). AF signal components are always highly contaminated by the ventricular complexes, and the cancellation of these components is never perfect. The remaining artifacts tend to induce erroneous DF estimates. In this paper, we report on the use of PRSA in the context of noninvasive AF classification procedures for improving DF estimation. The potential of PRSA is demonstrated by experiments both on synthetic and clinical ECG signals.     

Steady-state MSE convergence of LMS adaptive filters withdeterministic reference inputs with applications to biomedical signals

We analyze the steady-state mean square error (MSE) convergence of the LMS algorithm when deterministic functions are used as reference inputs. A particular adaptive linear combiner is presented where the reference inputs are any set of orthogonal basis functions-the adaptive orthogonal linear combiner (AOLC). Several authors have applied this structure always considering in the analysis a time-average behavior over one signal occurrence. We make a more precise analysis using the deterministic nature of the reference inputs and their time-variant correlation matrix. Two different situations are considered in the analysis: orthogonal complete expansions and incomplete expansions. The steady-state misadjustment is calculated using two different procedures with equivalent results: the classical one (analyzing the transient behavior of the MSE) and as the residual noise at the output of the equivalent time-variant transfer function of the system. The latter procedure allows a very simple formalism being valid for colored noise as well. The derived expressions for steady-state misadjustment are contrasted with experimental results in electrocardiographic (ECG) signals, giving exact concordance for any value of the step size     

Removal of the eye-blink artifacts from EEGs via STF-TS modeling and robust minimum variance beamforming

A novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on a novel space-time-frequency (STF) model of EEGs and robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, namely, an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, the vector corresponding to the spatial distribution of the EB factor, is identified using the STF model of EEGs, provided by the parallel factor analysis (PARAFAC) method. In order to reduce the computational complexity present in the estimation of the STF model using the three-way PARAFAC, the time domain is subdivided into a number of segments, and a four-way array is then set to estimate the STF-time/segment (TS) model of the data using the four-way PARAFAC. The correct number of the factors of the STF model is effectively estimated by using a novel core consistency diagnostic- (CORCONDIA-) based measure. Subsequently, the STF-TS model is shown to closely approximate the classic STF model, with significantly lower computational cost. The results confirm that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements.     

Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm

A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm [1] has achieved notable success in still image coding. We modified the algorithm for the one-dimensional case and applied it to compression of ECG data. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate.     

Nonlinear adaptive prediction of complex-valued signals by complex-valued PRNN

A complex-valued pipelined recurrent neural network (CPRNN) for nonlinear adaptive prediction of complex nonlinear and nonstationary signals is introduced. This architecture represents an extension of the recently proposed real-valued PRNN of Haykin and Li in 1995. To train the CPRNN, a complex-valued real time recurrent learning (CRTRL) algorithm is first derived for a single recurrent neural network (RNN). This algorithm is shown to be generic and applicable to general signals that have complex domain representations. The CRTRL is then extended to suit the modularity of the CPRNN architecture. Further, to cater to the possibly large dynamics of the input signals, a gradient adaptive amplitude of the nonlinearity within the neurons is introduced to give the adaptive amplitude CRTRL (AACRTRL). A comprehensive analysis of the architecture and associated learning algorithms is undertaken, including the role of the number of nested modules, number of neurons within the modules, and input memory of the CPRNN. Simulations on real-world and synthetic complex data support the proposed architecture and algorithms.     

基于自适应Volterra的高斯噪声图像滤波算法

针对高斯噪声图像滤波问题,提出一种可以保留细节的自适应Volterra-Volterra滤波方法。为了解决二阶Volterra滤波器核系数确定难、数量多等问题,提出了一种自适应建核结构及算法。通过图像处理实验,与其它滤波算法进行了细节度保留、均方差(MSE)和信噪比(SNR)等指标的比较,结果表明,本文方法优于进行对比的其它滤波方法。     

The median frequency of the ECG during ventricular fibrillation:its use in an algorithm for estimating the duration of cardiac arrest

Recent studies have suggested that the initial therapeutic intervention for ventricular fibrillation (VF) may depend on downtime (DT), i.e., the time duration of VF. We characterized the dynamics of the frequency distribution in the power spectrum of the ECG recorded from eleven swine during VF to determine if enough information existed in this domain to estimate DT. We used the median frequency (FM) of the power spectrum to track the frequency distribution. The FM followed a dynamic repeatable course during the first 10 min of VF. Intersubject variability was small. We modeled the FM data of the eleven subjects with a set of first-order polynomial equations and tested the algorithm with data from an additional ten subjects. The algorithm predicted VF duration with an average error of -0.86 min; 71.5% of the predictions fell within the 95% confidence limits of the model. This paper has identified a signal processing tool which may be useful in the prehospital treatment of VF.     

利用非线性自适应技术检测微弱的瞬时信号

本文根据局部流形理论,提出了一类非线性自适应预测滤波模型。仿真结果显示,本文提出的自适应预测滤波模型具有很强的非线性近似能力,可以检测淹没在超混沌或杂波环境中的瞬时信号。     

Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis

Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been studied to identify areas related to EEG events. EEG data recorded in the magnetic resonance (MR) scanner with MR imaging is suffered from two specific artifacts, imaging artifact, and ballistocardiogram (BCG). In this paper, we focus on BCG. In preceding studies, average subtraction was often used for this purpose. However, average subtraction requires an assumption that BCG waveforms are precisely periodic, which seems unrealistic because BCG is a biomedical artifact. We propose the application of independent component analysis (ICA) with a postprocessing of high-pass filtering for the removal of BCG. With this approach, it is not necessary to assume that the BCG waveform is periodic. Empirically, we show that our proposed method removes BCG artifacts as well as does the average subtraction method. Power spectral density analysis of the two approaches shows that, with ICA, distortion of recovered EEG data is also as small as that associated with the average subtraction approach. We also propose a hypothesis for how head movement causes BCGs and show why ICA can remove BCG artifacts arising from this source.     

Removal of Base-Line Wander and Power-Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps

Linear phase filtering is proposed for the removal of baseline wander and power-line frequency components in electrocardiograms. In order to reduce the large number of computations involved in the digital filtering that are necessary, the desired filter spectrum was defined periodically. Making use of the property that the spectrum period is 50 Hz, the spectrum can be realized with a considerably reduced number of impulse response coefficients. This, in combination with the necessary impulse response symmetry, leads to a reduction in the number of multiplications per output sample by a factor of 10. A suitable impulse response is designed with a pass-band ripple of less than 0.5 dB and a high stop-band attenuation. The applicability is demonstrated by applying the filtering to exercise electrocardiograms.     

Estimation of the ventricular fibrillation duration byautoregressive modeling

An accurate estimation of ventricular fibrillation (VF) duration could be critical in selecting the most effective therapeutic intervention. The authors test the hypothesis that changes in frequency content of VF signals can be quantified by using autoregressive (AR) modeling, and the duration since the onset of VF can be estimated by using this method. VF signals were recorded for up to 300 s in five isolated rabbit hearts. Fourth-order AR parameters of successive segments were estimated, and frequencies of the first poles (the pole with lower frequency) were pooled together and a curve was fitted: F(t)=Aexp(-α_t)+B, where F(t) is the estimated frequency of the first pole at t'th time instant, α is the decay constant, B is the offset frequency, and A is the frequency at time zero minus the offset frequency. The utility of this curve in estimating the VF duration was tested in four new experiments, and the difference between the actual and the estimated VF duration (estimation error) was calculated. F(t), the frequency of the first pole, decreased from 12 to 6 Hz with duration of VF, while the frequency of the other pole decreased from 25 to 20 Hz. Parameters of the fitted curve were calculated as A=7.8, α=0.0041 and B was selected as four. Testing on a new set of VF signals produced very little estimation error for the first 100 s of VF, although this error increased with VF duration, For these new signals, the mean value of the absolute estimation error was 26 s. Results of this study show that changes in the frequency content of electrocardiogram (ECG) during VF can be quantified by AR modeling and that the frequency changes associated with a pole of this model can be used to estimate the VF duration     

Frequency estimation of electric signals based on the adaptive short-time Fourier transform

Frequency is an important parameter in the monitoring, control, and protection of power systems and electric equipment. In this article, based on the adaptive short-time Fourier transform, a new digital algorithm is presented to estimate the frequency of electric signals. The novel window, called the Rectangle Self-Convolution Window, is adapted to eliminate the impact of harmonics, and the window length is adjusted according to the variation of signal frequency. The algorithm is easy to implement with high precision and short response time as well as a wide measuring range. It is thus suitable for real-time detection, especially when the frequency changes abruptly and the signal is corrupted by harmonics. Simulation results and practical application show that it is feasible and effective under different conditions.     

Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties

Time-frequency analysis is considered for characterizing atrial fibrillation in the surface electrocardiogram (ECG). Variations in fundamental frequency of the fibrillatory waves are tracked by using different time-frequency distributions which are appropriate to short- and long-term variations. The cross Wigner-Ville distribution is found to be particularly useful for short-term analysis due to its ability to handle poor signal-to-noise ratios. In patients with chronic atrial fibrillation, substantial short-term variations exist in fibrillation frequency and variations up to 2.5 Hz can be observed within a few seconds. Although time-frequency analysis is performed independently in each lead, short-term variations in fibrillation frequency often exhibit a similar pattern in the leads V1, V2 and V3. Using different techniques for short- and long-term analysis, it is possible to reliably detect subtle long-term changes in fibrillation frequency, e.g., related to an intervention, which otherwise would have been obscured by spontaneous variations in fibrillation frequency.     

A production demonstration of wafer-to-wafer plasma gate etchcontrol by adaptive real-time computation of the over-etch time from insitu process signals

An adaptive nonlinear controller for wafer-to-wafer plasma etch control is described. It uses real-time process signatures and historical data from a relational database for a computation of the over-etch time for the current wafer etching within the reactor. For an MOS gate etch the standard deviation of the oxide thickness between the gate and the source (or drain) is in the range of 10 Å. This is comparable to open-loop control or timed etch where the operator selects the ideal over-etch time. The controller has thus achieved a minimum of human equivalence and often performs better by 40%     

On the use of 2-D coding techniques for ECG signals.

This paper discusses the convenience of using two-dimensional (2-D) coding techniques for the compression of electrocardiogram (ECG) signals. These signals present a very clear periodicity that can be exploited by the use of a 2-D time/frequency transform to decorrelate it as much as possible. A brief theoretical approach is given to justify the use of this technique, and a comparison is made between a 2-D and a one-dimensional (1-D) uniform quantization scenarios. The influence of the error as well as the frame size on the estimation of the fundamental period is studied.     

联合多脉冲检测的PSWF正交调制信号盲自适应均衡

卫星通信信道的复杂时变特性,使基于椭圆球面波函数(Prolate Spheroidal Wave Function,PSWF)的正交调制信号脉冲组的正交性受到破坏,已有均衡方法未能充分利用多脉冲干扰中的有用信息,效果有限。针对该问题,结合信道均衡与多脉冲检测各自的优势,提出一种联合多脉冲检测的PSWF时域正交调制信号自适应均衡方法,利用多脉冲检测消除脉冲间干扰的能力,降低均衡模块的阶数及算法难度;同时,利用均衡模块对信道的部分补偿作用,为多脉冲检测改善信道环境。在相同信道条件下,所提方法获得同等量级误比特率所需信噪比较自适应判决反馈均衡算法降低约2 dB。