Real-time Artifact Research Articles

Filtering of ECG signals distorted by magnetic field gradients during MRI using non-linear filters and higher-order statistics

The electrocardiogram (ECG) is the state-of-the-art signal for patient monitoring and gating in cardiovascular magnetic resonance (CMR) imaging applications. However, ECG signals are severely distorted during MRI scans due to the effects of static magnetic fields, radio frequency pulses and fast-switching gradient magnetic fields. Gradient-induced artifacts that cause high frequency peaks in the ECG signal especially hamper a correct and reliable QRS detection. To cope with this problem, a new median-based real-time gradient filter (M1) approach was developed. To improve the filter results, a preprocessing step based on higher-order statistics (M2) was added to this. For the evaluation of the filtering techniques, ECG signals were acquired in a 3T MRI scanner during different MR sequences. A qualitative comparison was made using the mean square error as well as the signal power before and after filtering and the results of the QRS detection. Here, reliable results were achieved (detection error rate [DER] M1: 0.23%, DER M2: 0.74%). It was shown that the two developed techniques allowed a reliable suppression of the gradient artifacts in real time.

On-line EEG Denoising and Cleaning Using Correlated Sparse Recovery and Active Learning.

We have developed two new methods that use sparse recovery and active learning techniques for near real-time artifact identification and removal in EEG recordings. The first algorithm, called Correlated Sparse Signal Recovery (CSSR) addresses the problem of structured sparse signal recovery when statistical rather than exact properties describing the structure of the signal are appropriate, as in the elimination of eye movement artifacts; such tasks cannot be done efficiently using structured models that assume a common sparsity profile of fixed groups of components. Our algorithm learns structured sparse coefficients in a Bayesian paradigm. Using it, we have successfully identified and subtracted eye movement (structured) artifacts in real EEG recordings resulting in minimal data loss. Our method outperforms ICA and standard sparse recovery algorithms by preserving both spectral and complexity properties of the denoised EEG. Our second method uses a new active selection algorithm that we call Output-based Active Selection (OAS). When applied to the task of detection of EEG epochs containing other non-structured artifacts from an ensemble of detectors, OAS boosts accuracy of the ensemble from 91% to 97.5% with only 10% active labels. Our methods can also be applied to real-time artifact removal in magnetoencephalography (MEG) and blood pressure signals.

A real-time artifact reduction algorithm based on precise threshold during short-separation optical probe insertion in neurosurgery

During neurosurgery, an optical probe has been used to guide the micro-electrode, which is punctured into the globus pallidus (GP) to create a lesion that can relieve the cardinal symptoms. Accurate target localization is the key factor to affect the treatment. However, considering the scattering nature of the tissue, the “look ahead distance (LAD)” of optical probe makes the boundary between the different tissues blurred and difficult to be distinguished, which is defined as artifact. Thus, it is highly desirable to reduce the artifact caused by LAD. In this paper, a real-time algorithm based on precise threshold was proposed to eliminate the artifact. The value of the threshold was determined by the maximum error of the measurement system during the calibration procession automatically. Then, the measured data was processed sequentially only based on the threshold and the former data. Moreover, 100[Formula: see text][Formula: see text]m double-fiber probe and two-layer and multi-layer phantom models were utilized to validate the precision of the algorithm. The error of the algorithm is one puncture step, which was proved in the theory and experiment. It was concluded that the present method could reduce the artifact caused by LAD and make the real boundary sharper and less blurred in real-time. It might be potentially used for the neurosurgery navigation.

Real-time EEG artifact correction during fMRI using ICA

BackgroundSimultaneous acquisition of EEG and fMRI data results in EEG signal contamination by imaging (MR) and ballistocardiogram (BCG) artifacts. Artifact correction of EEG data for real-time applications, such as neurofeedback studies, is the subject of ongoing research. To date, average artifact subtraction (AAS) is the most widespread real-time method used to partially remove BCG and imaging artifacts without requiring extra hardware equipment; no alternative software-only real time methods for removing EEG artifacts are available. New methodsWe introduce a novel, improved approach for real-time EEG artifact correction during fMRI (rtICA). The rtICA is based on real time independent component analysis (ICA) and it is employed following the AAS method. The rtICA was implemented and validated during EEG and fMRI experiments on healthy subjects. ResultsOur results demonstrate that the rtICA employed after the rtAAS can obtain 98.4% success in detection of eye blinks, 4.4 times larger INPS reductions compared to RecView-corrected data, and effectively reduce motion artifacts, as well as imaging and muscle artifacts, in real time on six healthy subjects. Comparison with existing methodsWe compared our real-time artifact reduction results with the rtAAS and various offline methods using multiple evaluation metrics, including power analysis. Importantly, the rtICA does not affect brain neuronal signals as reflected in EEG bands of interest, including the alpha band. ConclusionsA novel real-time ICA method was proposed for improving the EEG quality signal recorded during fMRI acquisition. The results show substantial reduction of different types of artifacts using real-time ICA method.

A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements

Objective. Non-invasive measurement of human neural activity based on the scalp electroencephalogram (EEG) allows for the development of biomedical devices that interface with the nervous system for scientific, diagnostic, therapeutic, or restorative purposes. However, EEG recordings are often considered as prone to physiological and non-physiological artifacts of different types and frequency characteristics. Among them, ocular artifacts and signal drifts represent major sources of EEG contamination, particularly in real-time closed-loop brain–machine interface (BMI) applications, which require effective handling of these artifacts across sessions and in natural settings. Approach. We extend the usage of a robust adaptive noise cancelling (ANC) scheme ( filtering) for removal of eye blinks, eye motions, amplitude drifts and recording biases simultaneously. We also characterize the volume conduction, by estimating the signal propagation levels across all EEG scalp recording areas due to ocular artifact generators. We find that the amplitude and spatial distribution of ocular artifacts vary greatly depending on the electrode location. Therefore, fixed filtering parameters for all recording areas would naturally hinder the true overall performance of an ANC scheme for artifact removal. We treat each electrode as a separate sub-system to be filtered, and without the loss of generality, they are assumed to be uncorrelated and uncoupled. Main results. Our results show over 95–99.9% correlation between the raw and processed signals at non-ocular artifact regions, and depending on the contamination profile, 40–70% correlation when ocular artifacts are dominant. We also compare our results with the offline independent component analysis and artifact subspace reconstruction methods, and show that some local quantities are handled better by our sample-adaptive real-time framework. Decoding performance is also compared with multi-day experimental data from 2 subjects, totaling 19 sessions, with and without filtering of the raw data. Significance. The proposed method allows real-time adaptive artifact removal for EEG-based closed-loop BMI applications and mobile EEG studies in general, thereby increasing the range of tasks that can be studied in action and context while reducing the need for discarding data due to artifacts. Significant increase in decoding performances also justify the effectiveness of the method to be used in real-time closed-loop BMI applications.

A bidirectional neural interface SoC with an integrated spike recorder, microstimulator, and low-power processor for real-time stimulus artifact rejection

This paper presents a neural interface system-on-chip (SoC) featuring combined spike recording, electrical microstimulation, and real-time stimulus artifact rejection (SAR) for bidirectional interfacing with the nervous system. The SoC integrates a spike-recording front-end with input noise voltage of 3.42 μVrms (0.5 Hz---50 kHz), microstimulating back-end for delivering charge-balanced monophasic or asymmetric biphasic current pulses of <100 μA with passive discharge, and μW-level digital signal processing (DSP) unit for real-time SAR based on template subtraction. The DSP unit initializes its embedded 16b, 4 K static random-access memory with the first recorded stimulus artifact to reduce the operation time in generating an accurate artifact template signal for subtraction. Fabricated in AMS 0.35 μm 2P/4M CMOS, the 3.1 × 3.1-mm2 SoC has been characterized in benchtop tests and neurobiological experiments with isolated buccal ganglia of an Aplysia californica (a marine mollusk). The SoC can successfully remove mV-range stimulus artifacts with duration up to ~115 ms from the contaminated neural data in real time and recover µV-range extracellular neural spikes that occur on the tail end of the artifacts. The average root-mean-square (rms) value of the pre-processed stimulus artifact is reduced by a factor of ~24---30 post-processing, with DSP unit power consumption of <25 µW from 1.5 V.

Ocular and cardiac artifact rejection for real-time analysis in MEG.

Recently, magnetoencephalography (MEG) based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods for neuroscience research. It is well known that artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming process. The method (referred to as ocular and cardiac artifact rejection for real-time analysis, OCARTA) is based on constrained independent component analysis (cICA), where a priori information of the underlying source signals is used to optimize and accelerate signal decomposition. Thereby, prior information is incorporated by using the subject's individual cardiac and ocular activity. The algorithm automatically uses different separation strategies depending on the underlying source activity. OCARTA was tested and applied to data from three different but most commonly used MEG systems (4D-Neuroimaging, VSM MedTech Inc. and Elekta Neuromag). Ocular and cardiac artifacts were effectively reduced within one iteration at a time delay of 1ms performed on a standard PC (Intel Core i5-2410M). The artifact rejection results achieved with OCARTA are in line with the results reported for offline ICA-based artifact rejection methods. Due to the fast and subject-specific signal decomposition the new approach introduced here is capable of real-time ocular and cardiac artifact rejection.

Ultrasound Artifacts

Ultrasound has become a widely used diagnostic imaging modality in medicine because of its safety and portability. Because of rapid advances in technology, in recent years, sonographic imaging quality has significantly increased. Despite these advances, the potential to encounter artifacts while imaging remains.This article classifies both common and uncommon gray-scale and Doppler ultrasound artifacts into those resulting from physiology and those caused by hardware. A brief applied-physics explanation for each artifact is listed along with an illustrated diagram. The imaging appearance of artifacts is presented in case examples, along with strategies to minimize the artifacts in real time or use them for clinical advantage where applicable.

A Constrained ICA Approach for Real-Time Cardiac Artifact Rejection in Magnetoencephalography

Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.

A Blink Restoration System With Contralateral EMG Triggered Stimulation and Real-Time Artifact Blanking

Patients suffering from facial paralysis are on the hazard of disfigurement and loss of vision due to loss of blink function. Functional-electrical stimulation (FES) is one possible way of restoring blink and other functions in these patients. A blink restoration system for uni-lateral facial paralyzed patients is described in this paper. The system achieves restoration of synchronized blink through processing the myoelectric signal of orbicularis oculi at the normal side in real-time as the trigger to stimulate the paralyzed eyelid. Design issues are discussed, including EMG processing, stimulating strategies and real-time artifact blanking. Two artifact removal approaches based on sample and hold and digital filtering technique are proposed and implemented. Finally, the whole system has been verified on rabbit models.

Corrections to “Real-Time Artifact Free Image Upscaling” [Oct 11 2760-2768

With regard to the above titled paper (ibid., vol. 20, no. 10, pp. 2760-2768, Oct. 2011), the authors have corrected some formulas that are affected by small errors (shifted indices, etc.). These errors had not been detected earlier, because the descriptions made the right ones rather evident; however, it is necessary to correct them. The corrections are presented here.

Kurtosis-Based Blind Source Extraction of Complex Non-Circular Signals with Application in EEG Artifact Removal in Real-Time

A new class of complex domain blind source extraction algorithms suitable for the extraction of both circular and non-circular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of non-circular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources.

Real-time artifact filtering in continuous VEPs/fMRI recording

Continuous recording of Visual Evoked Potentials (VEPs) and functional Magnetic Resonance Imaging (fMRI) exploits the VEPs high temporal resolution and the fMRI high spatial resolution. In this work, we present a new method of continuous VEPs/fMRI recording to study visual function in seven normal subjects. Our real-time artifact filtering is characterized by a procedure based on an analytical study of echo-planar imaging (EPI) sequence parameters related electro-encephalogram (EEG)-artifact shapes. The magnetic field artifacts were minimized by using a dedicated amagnetic device and by a subtraction algorithm that takes into account the EPI sequence parameters. No significant decrease in signal-to-noise ratio was observed in case of EEG recording simultaneously with MR acquisition; similarly, transient and steady-state VEPs parameters were comparable during fMRI acquisition and in the off-phase of fMRI recording. We also applied this method to one patient with optic neuritis, and, compared with controls, found different results. We suggest that our technique can be reliably used to investigate the function of human visual cortex and properly correlate the electrophysiological and functional neuroimaging related changes.

Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA

Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the mother should be monitored continuously to provide an important indication of current well-being. Due to physical constraints of an fMEG system, it is not possible to use clinically established heart monitors for this purpose. Considering this constraint, we developed a real-time heart monitoring system for biomagnetic measurements and showed its reliability and applicability in research and for clinical examinations. The developed system consists of real-time access to fMEG data, an algorithm based on Independent Component Analysis (ICA), and a graphical user interface (GUI). The algorithm extracts the current fetal and maternal heart signal from a noisy and artifact-contaminated data stream in real-time and is able to adapt automatically to continuously varying environmental parameters. This algorithm has been named Adaptive Real-time ICA (ARICA) and is applicable to real-time artifact removal as well as to related blind signal separation problems.

Real time ECG artifact removal for myoelectric prosthesis control

The electrocardiogram (ECG) artifact is a major noise source contaminating the electromyogram (EMG) of torso muscles. This study investigates removal of ECG artifacts in real time for myoelectric prosthesis control, a clinical application that demands speed and efficiency. Three methods with simple and fast implementation were investigated. Removal of ECG artifacts by digital high-pass filtering was implemented. The effects of the cutoff frequency and filter order of high-pass filtering on the resulting EMG signal were quantified. An alternative adaptive spike-clipping approach was also developed to dynamically detect and suppress the ECG artifacts in the signal. Finally, the two methods were combined. Experimental surface EMG recordings with different ECG/EMG ratios were used as testing signals to evaluate the proposed methods. As a key parameter for clinical myoelectric prosthesis control, the average rectified amplitude of the signal was used as the performance indicator to quantitatively analyze the EMG content distortion and the ECG artifact suppression imposed by the two methods. Aiming at clinical application, the optimal parameter assignment for each method was determined on the basis of the performance using the suite of testing signals with various ECG/EMG ratios.

A method for real-time artifact filtering during simultaneous EEG/fMRI acquisition: preliminary results

A method for real-time artifact filtering during simultaneous EEG/fMRI acquisition: preliminary results

A method for real-time artifact filtering during simultaneous EEG/fMRI acquisition: preliminary results

A standard EEG equipment was modified in order to work properly during ultra-fast MRI acquisitions. Changes include: amagnetic electrode cap, optical fiber link, shielding box for EEG amplifier, twisted low metal mass cable. The effects of the RF pulse and time varying magnetic fields were minimized by using a correct head cap wires locked environment montage and then removed with a simple subtraction algorithm during EEG/fMRI acquisition. Ballistocardiogram artifact was also minimized at suitable levels by using a comfortable head position and immobilization.

Real-time MR artifacts filtering during continuous EEG/fMRI acquisition.

The purpose of this study was the development of a real-time filtering procedure of MRI artifacts in order to monitor the EEG activity during continuous EEG/fMRI acquisition. The development of a combined EEG and fMRI technique has increased in the past few years. Preliminary "spike-triggered" applications have been possible because in this method, EEG knowledge was only necessary to identify a trigger signal to start a delayed fMRI acquisition. In this way, the two methods were used together but in an interleaved manner. In real simultaneous applications, like event-related fMRI study, artifacts induced by MRI events on EEG traces represent a substantial obstacle for a right analysis. Up until now, the methods proposed to solve this problem are mainly based on procedures to remove post-processing artifacts without the possibility to control electrophysiological behavior of the patient during fMRI scan. Moreover, these methods are not characterized by a strong "prior knowledge" of the artifact, which is an imperative condition to avoid any loss of information on the physiological signals recovered after filtering. In this work, we present a new method to perform simultaneous EEG/fMRI study with real-time artifacts filtering characterized by a procedure based on a preliminary analytical study of EPI sequence parameters-related EEG-artifact shapes. Standard EEG equipment was modified in order to work properly during ultra-fast MRI acquisitions. Changes included: high-performance acquisition device; electrodes/cap/wires/cables materials and geometric design; shielding box for EEG signal receiver; optical fiber link; and software. The effects of the RF pulse and time-varying magnetic fields were minimized by using a correct head cap wires-locked environment montage and then removed during EEG/fMRI acquisition with a subtraction algorithm that takes in account the most significant EPI sequence parameters. The on-line method also allows a further post-processing utilization.

Real-time pulse oximetry artifact annotation on computerized anaesthetic records.

Adoption of computerised anaesthesia record keeping systems has been limited by the concern that they record artifactual data and accurate data indiscriminately. Data resulting from artifacts does not reflect the patient's true condition and presents a problem in later analysis of the record, with associated medico-legal implications. This study developed an algorithm to automatically annotate pulse oximetry artifacts and sought to evaluate the algorithm's accuracy in routine surgical procedures. MacAnaesthetist is a semi-automatic anaesthetic record keeping system developed for the Apple Macintosh computer, which incorporated an algorithm designed to automatically detect pulse oximetry artifacts. The algorithm labeled artifactual oxygen saturation values < 90%. This was done in real-time by analyzing physiological data captured from a Datex AS/3 Anaesthesia Monitor. An observational study was conducted to evaluate the accuracy of the algorithm during routine surgical procedures (n = 20). An anaesthetic record was made by an anaesthetist using the Datex AS/3 record keeper, while a second anaesthetic record was produced in parallel using MacAnaesthetist. A copy of the Datex AS/3 record was kept for later review by a group of anaesthetists (n = 20), who judged oxygen saturation values < 90% to be either genuine or artifact. MacAnaesthetist correctly labeled 12 out of 13 oxygen saturations < 90% (92.3% accuracy). A post-operative review of the Datex AS/3 anaesthetic records (n = 8) by twenty anaesthetists resulted in 127 correct responses out of total of 200 (63.5% accuracy). The remaining Datex AS/3 records (n = 12) were not reviewed, as they did not contain any oxygen saturations <90%. The real-time artifact detection algorithm developed in this study was more accurate than anaesthetists who post-operatively reviewed records produced by an existing computerised anaesthesia record keeping system. Algorithms have the potential to more accurately identify and annotate artifacts on computerised anaesthetic records, assisting clinicians to more correctly interpret abnormal data.

Real-time direct volume rendering in functional magnetic resonance imaging.

Direct volume rendering is a visualization method that allows display of all information hidden in three-dimensional data sets of, for example, computed tomography or magnetic resonance imaging (MRI). In contrast to commonly used surface rendering methods, these algorithms need no preprocessing but suffer from a high computational complexity. A real-time rendering system, VIRIM (Vitec: Visualization Technology GmbH, Mannheim, Germany), cuts down rendering times of minutes on normal workstations to an interactive rate of 1 second or less. The immediate visual feedback allows interactive steering of the visualization process to achieve insight into the internal three-dimensional structure of objects. Additional information is obtained by using an interactive gray-value segmentation tool that both allows segmentation of the data set according to bone, tissue, and liquor and display of multifunctional data sets (e.g., functional MRI [fMRI] data sets). Thus, real-time direct volume rendering allows segmentation and volume data processing of functional and anatomical MR data sets simultaneously. As this method can be integrated in the clinical routine, it is of great importance for real-time motion artifact detection and the interpretation of fMRI data acquired during cognitive experiments with normal subjects and psychiatric patients. Because of the free programmability of VIRIM, more complex matching procedures are currently being investigated for future implementation.