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.     

CPR artifact removal from human ECG using optimal multichannel filtering

The purpose of this study was to assess whether the artifacts presented by precordial compressions during cardiopulmonary resuscitation could be removed from the human electrocardiogram (ECG) using a filtering approach. This would allow analysis and defibrillator charging during ongoing precordial compressions yielding a very important clinical improvement to the treatment of cardiac arrest patients. In this investigation we started with noise-free human ECGs with ventricular fibrillation (VF) and ventricular tachycardia (VT) records. To simulate a realistic resuscitation situation, we added a weighted artifact signal to the human ECG, where the weight factor was chosen to provide the desired signal-to-noise ratio (SNR) level. As artifact signals we used ECGs recorded from animals in asystole during precordial compressions at rates 60, 90, and 120 compressions/min. The compression depth and the thorax impedance was also recorded. In a real-life situation such reference signals are available and, using an adaptive multichannel Wiener filter, we construct an estimate of the artifact signal, which subsequently can be subtracted from the noisy human ECG signal. The success of the proposed method is demonstrated through graphic examples, SNR, and rhythm classification evaluations.     

An optimal design method for magnetic resonance imaging gradient waveforms

A method of using nonlinear constrained optimization to design gradient waveforms for magnetic resonance imaging is described. Formulation and solution of the waveform optimization problem are described and example waveforms are presented for a variety of design objectives and constraint sets. Most design objectives can be expressed as linear or quadratic functions of the discrete parameter set, and most constraint functions are linear. Thus, linear and quadratic programming techniques can be utilized to solve the optimization problem. Among the objectives considered are: minimize RMS current; minimize waveform slewing; minimize waveform moments to reduce motion induced dephasing; minimize echo time (TE) for given imaging and motion refocusing conditions; maximize the gradient amplitude during RF application and sampling and the area of the phase encoding waveform to maximize resolution; and minimize or maximize the gradient b factor or diffusion sensitivity. This optimal design procedure produces physically realizable waveforms which optimally achieve specific imaging and motion artifact reduction goals, and it is likely to reduce waveform design time by making it more scientifically (rather than heuristically) based.     

Estimation of QRS complex power spectra for design of a QRS filter

We present power spectral analysis of ECG waveforms as well as isolated QRS complexes and episodes of noise and artifact. The power spectral analysis shows that the QRS complex could be separated from other interfering signals. A bandpass filter that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio would be of use in many ECG monitoring instruments. We calculate the coherence function and, from that, the signal-to-noise ratio. Upon carrying out this analysis on experimentaly obtained ECG data, we observe that a bandpass filter with a center frequency of 17 Hz and a Q of 5 yields the best signal-to-noise ratio.     

脑电信号预处理方法研究综述

脑电信号是一种复杂且重要的生物信号,被广泛应用于类脑智能技术和脑机接口领域的研究。文中介绍了干扰正常脑电信号的常见非生理性伪迹和生理性伪迹的类型及特点,并对生理性伪迹的产生原因进行了详细分析。通过对各种脑电信号去除伪迹方法的回顾以及应用现状的分析,比较并总结了传统去除伪迹方法和新型去除伪迹方法的研究进展,并进一步分析去除伪迹方法的优缺点。部分方法已经成功应用于处理脑电信号中的眼电、心电和肌电等伪迹中。文中还针对目前脑电信号去除伪迹的需求及所面临的问题给出了应对策略,并对未来的研究方向进行了分析和展望。     

A respiratory motion artifact reduction method in magnetic resonance imaging of the chest

An image reconstruction algorithm based on the assumption that the respiratory motion of the chest is linear in space and arbitrary in time is presented. The linear respiratory motion causes phase distortion on the magnetic resonance (MR) data. As a result of this motion, the MR data are the samples of the Fourier transform of the spin density on a nonrectangular grid. In image reconstruction. before taking the inverse Fourier transform, the phase distortion is compensated for, and the rectangular samples are interpolated from the existing nonrectangular samples. A significant amount of motion artifact suppression is achieved with a rough knowledge on the motion using this method. It is demonstrated that the respiratory motion model parameters can be estimated using the information hidden in the motion artifacts.     

CPR Artifact Removal in Ventricular Fibrillation ECG Signals Using Gabor Multipliers

Background and objective: We present an algorithm for discarding cardiopulmonary resuscitation (CPR) components from ventricular fibrillation ECG (VF ECG) signals and establish a method for comparing CPR attenuation on a common dataset. Removing motion artifacts in ECG allows for uninterrupted rhythm analysis and reduces ldquohands-offrdquo time during resuscitation. Methods and results: The current approach assumes a multichannel setting where the information of the corrupted ECG is combined with an additional pressure signal in order to estimate the motion artifacts. The underlying algorithm relies on a localized time--frequency transformation, the Gabor transform, that reveals the perturbation components, which, in turn, can be attenuated. The performance of the method is evaluated on a small set of test signals in the form of error analysis and compared to two well-established CPR removal algorithms that use an adaptive filtering system and a state--space model, respectively. Conclusion: We primarily point out the potential of the algorithm for successful artifact removal; however, on account of the limited set of human VF and animal asystole CPR signals, we refrain from a statistical analysis of the efficiency of CPR attenuation. The results encourage further investigations in both the theoretical and the clinical setup.     

Acoustic FMRI noise: linear time-invariant system model

Functional magnetic resonance imaging (fMRI) enables sites of brain activation to be localized in human subjects. For auditory system studies, however, the acoustic noise generated by the scanner tends to interfere with the assessments of this activation. Understanding and modeling fMRI acoustic noise is a useful step to its reduction. To study acoustic noise, the MR scanner is modeled as a linear electroacoustical system generating sound pressure signals proportional to the time derivative of the input gradient currents. The transfer function of one MR scanner is determined for two different input specifications: 1) by using the gradient waveform calculated by the scanner software and 2) by using a recording of the gradient current. Up to 4 kHz, the first method is shown as reliable as the second one, and its use is encouraged when direct measurements of gradient currents are not possible. Additionally, the linear order and average damping properties of the gradient coil system are determined by impulse response analysis. Since fMRI is often based on echo planar imaging (EPI) sequences, a useful validation of the transfer function prediction ability can be obtained by calculating the acoustic output for the EPI sequence. We found a predicted sound pressure level (SPL) for the EPI sequence of 104 dB SPL compared to a measured value of 102 dB SPL. As yet, the predicted EPI pressure waveform shows similarity as well as some differences with the directly measured EPI pressure waveform.      

MRI artifact cancellation due to rigid motion in the imaging plane

A post-processing technique has been developed to suppress the magnetic resonance imaging (MRI) artifact arising from object planar rigid motion. In two-dimensional Fourier transform (2-DFT) MRI, rotational and translational motions of the target during magnetic resonance magnetic resonance (MR) scan respectively impose nonuniform sampling and a phase error an the collected MRI signal. The artifact correction method introduced considers the following three conditions: (1) for planar rigid motion with known parameters, a reconstruction algorithm based on bilinear interpolation and the super-position method is employed to remove the MRI artifact, (2) for planar rigid motion with known rotation angle and unknown translational motion (including an unknown rotation center), first, a super-position bilinear interpolation algorithm is used to eliminate artifact due to rotation about the center of the imaging plane, following which a phase correction algorithm is applied to reduce the remaining phase error of the MRI signal, and (3) to estimate unknown parameters of a rigid motion, a minimum energy method is proposed which utilizes the fact that planar rigid motion increases the measured energy of an ideal MR image outside the boundary of the imaging object; by using this property all unknown parameters of a typical rigid motion are accurately estimated in the presence of noise. To confirm the feasibility of employing the proposed method in a clinical setting, the technique was used to reduce unknown rigid motion artifact arising from the head movements of two volunteers.     

T2 restoration and noise suppression of hybrid MR images using Wiener and linear prediction techniques

The authors address the problem of enhancing hybrid magnetic resonance (MR) images degraded by T2 effects and additive measurement noise. To reduce imaging time, MR signals are acquired using hybrid imaging (HI) sequences such as rapid acquisition relaxation-enhanced (RARE) and fast spin-echo (FSE). With these techniques, T2 effects act as a distortion filter. This T2 filter affects the signal and results in image spatial resolution and/or contrast loss. Furthermore, the amplitude and phase discontinuities in the T2 filter frequency response function may generate serious ringing artifacts. These distortions will damage image quality and affect object detectability. The authors use the Wiener filter and linear prediction (LP) technique to process HI MR signals in the spatial frequency domain (K-space) and the hybrid domain, respectively. Based on the average amplitude symmetry constraint of the spin echo signal, the amplitude frequency response function of the T2 distortion filter can be estimated and used in the Wiener filter for a global T2 amplitude restoration. Then, the linear prediction technique is utilized to obtain the local signal amplitude and phase estimates around the discontinuities of the frequency response function of the T2 filter. These estimates are used to make local amplitude and phase corrections. The effectiveness of this combined technique in correcting T2 distortion and reducing the measurement noise is analyzed and demonstrated using experiments on both phantoms and human studies.     

Analysis and correction of count rate reduction during simultaneous MR-PET measurements with the BrainPET scanner

In hybrid magnetic resonance-positron emission tomography (MR-PET) studies with the Siemens 3T MR-BrainPET scanner an instantaneous reduction of the PET sensitivity was observed during execution of certain MR sequences. This interference was investigated in detail with custom-made as well as standard clinical MR sequences. The radio-frequency pulses, the switched gradient fields and the constant magnetic field were examined as the relevant parameters of the magnetic resonance imaging (MRI) system as well as the air temperature within the PET detectors. Our investigation comprised the analysis of the analog PET signals, the total count rates, the geometric distribution of the count rate reduction within the BrainPET detector as well as reconstructed images. The fast switching magnetic field gradients were identified to distort the analog PET detector signals. The measured count rate reduction was found to be less than 3%, but only up to 2% in the case of echo planar imaging sequences, as applied in functional MRI. For clinical sequences routinely used in hybrid MR-BrainPET measurements, a correction method has been designed, implemented, and evaluated .     

基于PDE去鬼影的自适应非均匀性校正算法研究

针对基于场景的自适应校正算法普遍存在鬼影的问题, 分析了神经网络算法(NN-NUC)产生鬼影的原因,并在此基础上提出了用基于偏微分方程(PDE)的非线性滤波方法取代NN-NUC算法中邻域平均的方法来获取期望图像,从而减少边缘像素误差,达到消除鬼影的目的.采用实际采集的红外图像进行实验,结果表明,很好地消除了鬼影.与已有的几种去鬼影的方法相比,具有更快的收敛性.     

Computer analysis of the electrocardiogram during esophageal pacingcardiac stress

It has been estimated that 15 to 30% of patients with suspected or known coronary artery disease are unable to perform an adequate exercise stress test due to a variety of reasons such as obesity, poor physical condition, claudication, etc. Transesophageal atrial pacing has been proposed as a noninvasive alternative for inducing cardiac stress in patients who cannot exercise. Although computer analysis is commonly employed to analyze the electrocardiogram (ECG) during the conventional exercise stress test, the surface ECG recorded during transesophageal atrial pacing is contaminated with large pacing artifacts which confound beat identification by standard computer software. We report the development of a robust signal processing algorithm for interpretation of the surface ECG during transesophageal atrial pacing stress. The algorithm employs novel schemes using both linear and nonlinear transformations to detect and differentiate between the pacing artifact and QRS complex even in difficult situations where the pacing artifact is in proximity to or superimposed on the QRS complex. The algorithm uses sophisticated logic for automatic recognition of sustained capture. It subsequently calculates beat-by-beat and average (over five beats) ST segment amplitude and slope. The algorithm also reports the instantaneous heart rate, RR interval, pace-to-R interval, R-wave amplitude, and estimated sinus node recovery time upon loss of sustained capture. The limitations of present exercise ECG computer methods in processing the ECG during transesophageal atrial pacing stress are evaluated and significantly improved performance by our algorithm is demonstrated.     

Minimisation of gradient artefacts in cardiac-MRI-gating using adaptive interference cancellation filter with synthesised reference

A simple and successful method for cardiac-MRI-gating is proposed. The adaptive interference cancellation filter (AICF) is used with a synthesised reference signal to reduce the gradient artefacts caused by the magnetic resonance (MR). The reference signals of the AICF were a combination of the noisy, three-channel ECG signals. In particular, the proposed method is based on a simple experimental setup and does not require any information from amplifiers of the MRI machine, such as shape, amplitude and rise time.     

An efficient feedback active noise control algorithm based on reduced-order linear predictive modeling of FMRI acoustic noise

Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.     

Robust adaptive extended Kalman filtering for real time MR-thermometry guided HIFU interventions

Real time magnetic resonance (MR) thermometry is gaining clinical importance for monitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. The requirement to resolve both physiological motion of mobile organs and the rapid temperature variations induced by state-of-the-art high-power HIFU systems require fast MRI-acquisition schemes, which are generally hampered by low signal-to-noise ratios (SNRs). This directly limits the precision of real time MR-thermometry and thus in many cases the feasibility of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data. Here, we propose a novel filter that aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy. For this, an adaptive extended Kalman filter using a model describing the heat transfer for acoustic heating in biological tissues was employed together with an additional outlier rejection to address the problem of sparse artifacted temperature points. The filter was compared to an efficient matched FIR filter and outperformed the latter in all tested cases. The filter was first evaluated on simulated data and provided in the worst case (with an approximate configuration of the model) a substantial improvement of the accuracy by a factor 3 and 15 during heat up and cool down periods, respectively. The robustness of the filter was then evaluated during HIFU experiments on a phantom and in vivo in porcine kidney. The presence of strong temperature artifacts did not affect the thermal dose measurement using our filter whereas a high measurement variation of 70% was observed with the FIR filter.     

Design and performance of adaptive systems based on structured stochastic optimization strategies

The theory and design of linear adaptive filters based on FIR filter structures is well developed and widely applied in practice. However, the same is not true for more general classes of adaptive systems such as linear infinite impulse response adaptive filters (MR) and nonlinear adaptive systems. This situation results because both linear IIR structures and nonlinear structures tend to produce multi-modal error surfaces for which stochastic gradient optimization strategies may fail to reach the global minimum. After briefly discussing the state of the art in linear adaptive filtering, the attention of this paper is turned to MR and nonlinear adaptive systems for potential use in echo cancellation, channel equalization, acoustic channel modeling, nonlinear prediction, and nonlinear system identification. Structured stochastic optimization algorithms that are effective on multimodal error surfaces are then introduced, with particular attention to the particle swarm optimization (PSO) technique. The PSO algorithm is demonstrated on some representative IIR and nonlinear filter structures, and both performance and computational complexity are analyzed for these types of nonlinear systems.     

可穿戴心电信号监测中运动伪影消除技术研究

心电图(ECG)是心脏疾病诊断最有效的工具。噪声的去除和Q波、R波、S波的提取是心电信号检测中的两大主题。本文使用Savitzky-Golay滤波器对人体在弯腰、走路、坐下-站起等运动状态下采集的心电信号进行分析,去除信号中的基线漂移和运动伪影,并对滤波后信号的Q波、R波和S波进行检测。通过将本文提出的滤波方式与卡尔曼滤波、小波分解就时间复杂度和功率谱密度两个参数进行对比分析,评估Savitzky-Golay滤波器在心电信号中运动伪影去除的优势。实验结果表明,Savitzky-Golay滤波器能更加有效地适应心电信号的变化,有效地去除心电信号中的噪声,并且最大限度保持心电波形的形状和波峰。      

Body movement activity recognition for ambulatory cardiac monitoring

Wearable electrocardiogram (W-ECG) recorders are increasingly in use by people suffering from cardiac abnormalities who also choose to lead an active lifestyle. The challenge presently is that the ECG signal is influenced by motion artifacts induced by body movement activity (BMA) of the wearer. The usual practice is to develop effective filtering algorithms which will eliminate artifacts. Instead, our goal is to detect the motion artifacts and classify the type of BMA from the ECG signal itself. We have recorded the ECG signals during specified BMAs, e.g., sitting still, walking, movements of arms and climbing stairs, etc. with a single-lead system. The collected ECG signal during BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts and sensor noise. A particular class of BMA is characterized by applying eigen decomposition on the corresponding ECG data. The classification accuracies range from 70% to 98% for various class combinations of BMAs depending on their uniqueness based on this technique. The above classification is also useful for analysis of P and T waves in the presence of BMA.     

Origin of the radio frequency pulse artifact in simultaneous EEG-fMRI recording: rectification at the carbon-metal interface

Simultaneous electroencephalograph-functional magnetic resonance imaging (EEG-fMRI) recording has become an important tool for investigating spatiotemporal properties of brain events, such as epilepsy, evoked brain responses, and changes in brain rhythms. Reduction of noise in EEG signals during fMRI recording is crucial for acquiring high-quality EEG-fMRI data. The main source of the noise includes the gradient artifact, the radio frequency (RF) pulse artifact, and the cardiac pulse artifact. Since the RF pulse artifact is relatively small in amplitude, little attention has been paid to this artifact, and its origin is not well understood. However, the amplitude of the RF pulse artifact fluctuates randomly even if a very high EEG sampling rate is used, making it more salient than the gradient artifact after postprocessing for noise removal. In this paper, we investigate the cause of the RF pulse artifact in EEG systems that use carbon wires.