Wavelet-based enhancement of signal-averaged electrocardiograms for late potential detection

An optimal wavelet filter to improve the signal-to-noise ratio (SNR) of the signal-averaged electrocardiogram is described. As the averaging technique leads to the best unbiased estimator, the challenge is to attenuate the noise while preserving the low amplitude signals that are usually embedded in it. An optimal, in the mean-square sense, wavelet-based filter has been derived from the model of the signal. However, such a filter needs exact knowledge of the noise statistic and the noise-free signal. Hence, to implement such a filter, a method based on successive sub-averaging and wavelet filtering is proposed. Its performance was evaluated using simulated and real ECGs. An improvement in SNR of between 6 and 10 dB can be achieved compared to a classical averaging technique which uses an ensemble of 64 simulated ECG beats. Tests on real ECGs demonstrate the utility of the method as it has been shown that by using fewer beats in the filtered ensemble average, one can achieve the same noise reduction. Clinical use of this technique would reduce the ensemble needed for averaging while obtaining the same diagnostic result.     

Design of a wavelet interpolation filter for enhancement of the ST-segment

A wavelet interpolation filter (WIF) is designed for the removal of motion artifacts in the ST-segment of stress ECGs. The WIF consists of two parts. One part is a wavelet transform that decomposes the stress ECG signal into several frequency bands using a Haar wavelet. The other part is an interpolation method, such as the spline technique, that is used to enhance the reconstruction performance of the signal decomposed by the wavelet transform. To evaluate the performance of the WIF, three indices are used: signal-to-noise ratio (SNR), reconstruction square error (RSE) and standard deviation (SD). The MIT/BIH arrhythmia database, the European ST-T database and the triangular wave are used for evaluation. A noisy ECG signal, corrupted by motion artifacts, is simulated by the addition of two types of random noise to the original ECG signal. For comparison, three indices for the other methods are also computed: mean, median and hard thresholding. The performance of the WIF shows that RSE, SNR and SD are 392.7, 18.3 dB and 2.6, respectively, in the case of a noisy signal with an SNR of 7.1 dB. This result is much better than those for the other methods.     

The derivation and verification of a non-stationary, optimal smoothing filter for nuclear medicine image data

A non-stationary optimal smoothing filter for digital nuclear medicine image data, degraded by Poisson noise, has been derived and applied to temporal simulated and clinical gated blood pool study (GBPS) data. The derived filter is automatically calculated from a large group (library) of similar GBPS which are representative of all studies acquired according to the same protocol in a defined patient population (the ensemble). The filter is designed to minimize the mean-square difference between the filtered data and the true image values; it provides an optimal trade-off between noise reduction and signal degradation for members of the ensemble. The filter is evaluated using a computer simulated ensemble of GBPS. Libraries of Poisson-degraded and non-degraded studies were generated. Libraries of up to 400 Poisson-degraded simulated studies were used to estimate optimal temporal filters that, when applied to Poisson-degraded members of the ensemble not included in the libraries, reduced the mean-square error in the raw data by 65%. When the non-degraded studies were used instead to compute the optimal filter values, the corresponding reduction in the error was 83%. Libraries of previously acquired clinical GBPS were then used to estimate optimal temporal filters for an ensemble of similarly acquired studies. These filters were subsequently applied to studies of 13 patients (not in the original libraries) who received multiple sequential repeat studies. Comparisons of both the filtered and raw data to averages of the repeat studies demonstrated that optimal filters calculated from 400 and 800 clinical studies reduced the mean-square error in the clinical data by 56% and 63% respectively.     

Multi-adaptive filtering technique for surface somatosensory evoked potentials processing

Somatosensory evoked potential (SEP) testing has been widely applied to diagnosis of various neurological disorders. However, SEP recorded using surface electrodes is buried in noises, which makes the signal-to-noise ratio (SNR) very poor. Conventional averaging method usually requires up to thousands of raw SEP input trials to increase the SNR so that an identifiable waveform can be produced for latency and amplitude measurement. In this study, a multi-adaptive filtering (MAF) technique, emerging from the combination of well-developed adaptive noise canceller and adaptive signal enhancer, is introduced for fast and accurate surface SEP extraction. The MAF technique first processes the raw surface recorded SEP by the Canceller with a reference noise channel of background noise for adaptive subtraction before entering the Enhancer. The MAF was verified by filtering simulated SEP signals in which electroencephalography and Gaussian noise of different SNRs were added. It was found that the MAF could effectively suppress the noise and enhance the SEP components such that the SNR of the SEP is improved. Results showed that MAF with 50 input trials could provide similar performance in SEP detection to those extracted by the conventional averaging method with 1000 trials even at an SNR of -20 dB.     

用奇异性检测技术提取诱发电位

本文介绍了用 小波变换模极大 值检测信号奇 异性的方法， 讨论了 信号与 白噪声 的小 波变换 和奇 异性指数之间区 别，模极大值沿 尺度 传递 的不同 特点 ，并 利用它 们的 区别 消除 诱发 电位 信号 中的噪 声，重 构出消 噪后 的信号， 取得 了较好 的效果 ，利用 较少的 刺激 次数就 可获取 诱发电 位信 号，有 效地提高 了信 噪比，大 大减 少了迭 加次数 ，特征 波的潜 伏期 及幅值 容易辨 认，易 于测量 ，且无 信号 失真。奇异性检 测技术有望成为 临床实用的诱 发电位提取 技术，并 可应用 于其他 生物 医学信 号的 消噪。     

Detection of brainstem auditory evoked potential by adaptive filtering

A method of detecting brainstem auditory evoked potential (BAEP) using adaptive signal enhancement (ASE) is proposed and tested in humans and cats. The ASE in this system estimates the signal component of the primary input, which is correlated with the reference input to the adaptive filter. The reference input is carefully designed to make an optimal and rapid estimation of the signal corrupted with noise, such as ongoing EEG. With a good choice of reference input, it is possible to track the variability of BAEP efficiently and rapidly. Moreover, the number of repetitions required could be markedly reduced and the result of the system is superior to that of ensemble averaging (EA). To detect BAEP in cats, only 30 ensemble averages are needed to obtain a reasonable reference input to the adaptive filter, and, for humans, 350–750 ensemble averages are sufficient for a satisfactory result. Using the LMS adaptive algorithm, individual BAEP can be obtained in real-time.     

基于小波和卡尔曼平滑的事件相关电位单次提取

本研究提出一种事件相关电位单次提取方法,可有效减少实验次数,并可探索实验之间ERP的变异性。此方法基于小波和卡尔曼平滑,首先利用小波变换考察ERP平均信号的时频特性,根据ERP不同分量出现的时间位置,在不同尺度上选取特定的单次实验ERP小波系数构成观测向量,其为真实ERP小波系数状态向量与噪声之和,然后对观测向量进行卡尔曼平滑,最后对卡尔曼平滑后的小波系数进行小波重构,得到单次提取的ERP信号。仿真实验表明,基于小波和卡尔曼平滑的方法不仅信噪比提高约16~18 dB,优于30次叠加平均、简单小波方法和基于高斯基函数的卡尔曼滤波方法,还可以跟踪ERP的幅度趋势变异性。与基于高斯基函数的卡尔曼滤波方法相比,所提方法降低了计算量。真实脑电ERP提取实验表明本方法较好地从单次记录中提取出了事件相关电位,并可解释ERP因适应和应激引起的趋势变异性。     

Arduino-based noise robust online heart-rate detection

This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.     

Robust non-homomorphic approach for speckle reduction in medical ultrasound images

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42 dB over the best state-of-the-art despeckling method reported in the literature, 1.73 dB over the Lee filter and 1.31 dB over the Kaun filter at an input SNR of 12.0 dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.     

Homomorphic wavelet thresholding technique for denoising medical ultrasound images

A novel homomorphic wavelet thresholding technique for reducing speckle noise in medical ultrasound images is presented. First, we show that the speckle wavelet coefficients in the logarithmically transformed ultrasound images are best described by the Nakagami family of distributions. By exploiting this speckle model and the Laplacian signal prior, a closed form, data-driven, and spatially adaptive threshold is derived in the Bayesian framework. The spatial adaptivity allows the additional information of the image (such as identification of homogeneous or heterogeneous regions) to be incorporated into the algorithm. Further, the threshold has been extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. Experimental results demonstrate the improved performance of the proposed method over other well-known speckle reduction filters. The application of the proposed method to a realistic US test image shows that the new technique, named HomoGenThresh, outperforms the best wavelet-based denoising method reported in [1] by more than 1.6 dB, Lee filter by 3.6 dB, Kaun filter by 3.1 dB and band-adaptive soft thresholding [2] by 2.1 dB at an input signal-to-noise ratio (SNR) of 13.6 dB.     

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.     

Cardiac and Respiratory MRI Gating Using Combined Wavelet Sub-Band Decomposition and Adaptive Filtering

Cardiac Magnetic Resonance Imaging (MRI) requires synchronization to overcome motion related artifacts caused by the heart’s contractions and the chest wall movements during respiration. Achieving good image quality necessitates combining cardiac and respiratory gating to produce, in real time, a trigger signal that sets off the consecutive image acquisitions. This guarantees that the data collection always starts at the same point of the cardiac cycle during the exhalation phase. In this paper, we present a real time algorithm for extracting a cardiac-respiratory trigger signal using only one, adequately placed, ECG sensor. First, an off-line calculation phase, based on wavelet decomposition, is run to compute an optimal QRS filter. This filter is used, afterwards, to accomplish R peak detection, while a low pass filtering process allows the retrieval of the respiration cycle. The algorithm’s synchronization capabilities were assessed during mice cardiac MRI sessions employing three different imaging sequences, and three specific wavelet functions. The prominent image enhancement gave a good proof of correct triggering. QRS detection was almost flawless for all signals. As for the respiration cycle retrieval it was evaluated on contaminated simulated signals, which were artificially modulated to imitate respiration. The results were quite satisfactory.     

Adaptive wavelet filtering for analysis of event-related potentials from the electro-encephalogram

A challenging task in psychophysiology is the extraction of event-related potentials (ERPs) from the background electro-encephalogram. The task is made more difficult by the properties of ERPs, which typically consist of multiple features of variable latency, localised in time and frequency. A novel technique is described for analysis of ERPs, adaptive wavelet filtering (AWF), which is proposed as an alternative to trial averaging. Band-limited detail representations of each trial are obtained using wavelet analysis. The Woody adaptive filter is then used to align trials with respect to the evoked response. In a simulation study, the AWF extracts 39% of higher-frequency signal variance from background noise, compared with less than 1% for standard averaging and the Woody filter. The AWF is applied to a data-set of 448 ERPs, comprising right-finger button presses from eight subjects. Average split-half reliability of the AWF on scales up to 12 Hz was 0.51.     

自适应阈值的小波噪声消除方法及其在消除心电图(ECG)噪声方面的应用

降低或者消除噪声,对得到有用的信号十分重要.例如像ECG这类非平稳信号,其噪声统计特性因为经常受各种因素的影响而变得十分复杂.在本文中,通过将应用小波进行噪声消除和B-Spline(B样条)噪声消除相结合的方法,得到一种新的信号噪声消除技术.试验证明,本文所提出的技术能够抑制噪声,并同时保留信号的细节特征.     

Noninvasive beat-to-beat detection of ventricular late potentials

The detection of ventricular late potentials is a subject of some clinical interest. Most techniques currently being investigated rely on signal averaging to extract the microvolt signals from the considerable amounts of noise which are present. Although this approach produces useful results, it does remove any beat-to-beat variations from the signal, and also requires that the signal be present for a considerable number of beats. The paper describes a technique for detecting ventricular late potentials from the body surface, which preserves beat-to-beat variations. The most important aspect of this technique is the use of an adaptive signal enhancer to minimise random noise. Representative results for one normal and two pathological subjects are presented and discussed. A comparison with signal averaging is made and the effectiveness of adaptive signal enhancement is illustrated.     

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.     

Adaptive filtering of evoked potentials using higher-order adaptive signal enhancer with genetic-type variable step-size prefilter

An adaptive signal enhancer based on third-order statistics with a genetictype, variable step-size prefilter is introduced to recover evoked potentials (EPs). EPs are usually embedded in the ongoing electroencephalogram with a very low signal-to-noise ratio (SNR). As a higher-order statistics technique has a natural tolerance to Gaussian noise, it is applicable for filtering EPs. An adaptive signal enhancer based on third-order statistics was used as the major filter in this study. Howver, the efficiency of the adaptive signal enhancer was reduced when the total power of uncorrelated noises was large. To improve the performance for EPs under poor SNR, a low-noise signal is required. Therefore a prefilter with a genetic-type, variable step-size algorithm was employed to enhance the SNR of the signal in this study. The fundamental idea of a genetic-type, variable step-size algorithm is that its step-sizes are regularly readjusted to optimum. Therefore this algorithm can be used as a prefilter with different noise levels. Experimental results showed that, for filtering EPs, the proposed scheme is superior to the adaptive signal enhancer with a normalised least mean square algorithm.     

An experimental evaluation of the use of an ensemble average for the calculation of turbulence in pulsatile flow

A comparison was made between turbulence calculated by subtracting an ensemble average from the instantaneous velocity and calculations made with a high pass digital filter. Velocity was measured with a laser Doppler anemometer in vitro in the region of a normal porcine aortic valve and in patients with a hot film anemometer in the region of normal aortic valves. From the velocity obtained in patients, the absolute turbulence intensity calculated using an ensemble average of 50 beats was nearly twice the turbulence intensity calculated using a digital filter. Individual beats sometimes showed differences of 150% compared to calculations based upon the use of a digital filter. Inspection showed that the ensemble average varied widely from the actual nonfluctuating velocity. Studies in vitro showed less beat to beat variation than occurred in patients. The absolute turbulence intensity measured in vitro, when calculated using an ensemble average, was only 20% greater than calculations using a digital filter. The differences were due primarily to beat-to-beat variations of the nonfluctuating velocity, but these beat-to-beat variations were less prominent than occurred in patients. These observations suggest that ensemble averaging may not be appropriate for the calculation of turbulence, particularly in patients.     

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short periodȁ9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.     

Data processing of stress ECGs using discrete cosine transform

The signal processing steps for the analysis of stress ECGs are aimed at improving the signal to noise ratio (SNR) of recordings in addition to eliminating artifacts due to respiration, movement of arms, etc. In this paper, we bring forth two important applications of the discrete cosine transform (DCT) for noise suppression and removal of baseline wander. The noise suppression algorithm has been framed on the basis of a two step procedure involving singular value decomposition (SVD) smoothing operation in transform domain followed by that in time domain. The mean square error (MSE) resulting from the first step is shown to effectively follow the trend obtained by using an ideal Wiener filter using DCT. In the second step, the degree of closeness to the minimum mean square error (MMSE) of the ideal Wiener filter is improved by subjecting the filtered outputs to a second SVD smoothing operation in time domain. Application of this scheme to noisy records has resulted in near perfect reproduction of the original noise free ECG without significant alterations in its morphological features.