Showing papers on "Artifact (error) published in 2007"

A Nonlinear Bayesian Filtering Framework for ECG Denoising

Abstract: In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.

Combined Use of Diffusion-Weighted MRI and 1H MR Spectroscopy to Increase Accuracy in Prostate Cancer Detection

Abstract: OBJECTIVE. The objective of our study was to establish the sensitivity and specificity for prostate cancer detection using a combined 1H MR spectroscopy and diffusion-weighted MRI approach.SUBJECTS AND METHODS. Forty-two men (mean age ± SD, 69.3 ± 4.7 years) with prostate cancer were studied using endorectal T2-weighted imaging, 2D chemical shift imaging (CSI), and isotropic apparent diffusion coefficient (ADC) maps. Regions of interest (ROIs) were drawn around the entire gland, central gland, and peripheral zone tumor, diagnostically defined as low signal intensity on T2-weighted images within a sextant that was biopsy-positive for tumor. Lack of susceptibility artifact on a gradient-echo B0 map through the slice selected for CSI and no high signal intensity on external array T1-weighted images confirmed the absence of significant hemorrhage after biopsy. CSI voxels were classified as nonmalignant or as tumor (ROI included ≥ 30% or 3 70% tumor). Choline-citrate (Cho/Cit) ratios and average ADCs were calc...

Artifact removal from EEG signals using adaptive filters in cascade

Abstract: Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

Sbi motion artifact removal apparatus and method

Abstract: A system, method and apparatus for eliminating image tearing effects and other visual artifacts perceived when scanning moving subject matter with a scanned beam imaging device. The system, method and apparatus uses a motion detection means in conjunction with an image processor to alter the native image to one without image tearing or other visual artifacts. The image processor monitors the motion detection means and reduces the image resolution or translates portions of the imaged subject matter in response to the detected motion.

The effect of motion on pulse oximetry and its clinical significance.

Abstract: Pulse oximetry is an important diagnostic and patient monitoring tool. However, motion can induce considerable error into pulse oximetry accuracy, resulting in loss of data, inaccurate readings, and false alarms. We will discuss how motion artifact affects pulse oximetry accuracy, the clinical consequences of motion artifact, and the methods used by various technologies to minimize the impact of the motion noise.

Online artifact removal for brain-computer interfaces using support vector machines and blind source separation

Abstract: We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described This filter is evaluated on three BCI datasets as a proof-of-concept of the method

Detection and removal of blemishes in digital images utilizing original images of defocused scenes

Abstract: A method automatically corrects dust artifact within images acquired by a system including a digital acquisition device including a lens assembly and a translucent lens cap. Multiple original digital images are acquired with the digital acquisition device. Probabilities that certain pixels correspond to dust artifact regions within the images are determined based at least in part on a comparison of suspected dust artifact regions within two or more of the images. Probable dust artifact regions are associated with extracted parameter values relating to the lens assembly when the images were acquired. A statistical dust map is formed including mapped dust regions based on the determining and associating. Pixels corresponding to correlated dust artifact regions are corrected within further digitally-acquired images based on the associated statistical dust map.

Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings

Abstract: Electroencephalographic (EEG) recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper the issue of artifact extraction from Electroencephalographic data is addressed and a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA), is presented and compared to two other techniques based on ICA and wavelet denoising. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts. This dataset was processed by WICA and the two other methods. The proposed technique had the best artifact separation performance for every kind of artifact also allowing for the minimum information loss.

Improving the Interpretation of Ictal Scalp EEG: BSS–CCA Algorithm for Muscle Artifact Removal

Abstract: Summary: Purpose: To investigate the potential clinical relevance of a new algorithm to remove muscle artifacts in ictal scalp EEG. Methods: Thirty-seven patients with refractory partial epilepsy with a well-defined seizure onset zone based on full presurgical evaluation, including SISCOM but excluding ictal EEG findings, were included. One ictal EEG of each patient was presented to a clinical neurophysiologist who was blinded to all other data. Ictal EEGs were first rated after band-pass filtering, then after elimination of muscle artifacts using a blind source separation–canonical correlation analysis technique (BSS–CCA). Degree of muscle artifact contamination, lateralization, localization, time and pattern of ictal EEG onset were compared between the two readings and validated against the other localizing information. Results: Muscle artifacts contaminated 97% of ictal EEGs, and interfered with the interpretation in 76%, more often in extratemporal than temporal lobe seizures. BSS–CCA significantly improved the sensitivity to localize the seizure onset from 62% to 81%, and performed best in ictal EEGs with moderate to severe muscle artifact contamination. In a significant number of the contaminated EEGs, BSS–CCA also led to an earlier identification of ictal EEG changes, and recognition of ictal EEG patterns that were hidden by muscle artifact. Conclusions: Muscle artifacts interfered with the interpretation in a majority of ictal EEGs. BSS–CCA reliably removed these muscle artifacts in a user-friendly manner. BSS–CCA may have an important place in the interpretation of ictal EEGs during presurgical evaluation of patients with refractory partial epilepsy.

Motion Artifact Reduction in Photoplethysmographic Signals using Singular Value Decomposition

Abstract: Artifact free photoplethysmographic (PPG) signals, obtained with red and infrared (IR) optical sources and detectors are necessary for non-invasive estimation of oxygen saturation (SpO2) in arterial blood. Movement of a patient corrupts the PPGs with motion artifacts, resulting in large errors in the computation of SpO2. This paper presents a novel singular value decomposition (SVD) based method to reduce motion artifacts from corrupted PPG signals. Test results on a prototype incorporating the proposed SVD technique show that stable and reliable SpO2 measurement is achieved even when PPGs are distorted by motion artifacts, thus establishing the efficacy of the proposed method.

Device and method for reducing effects of video artifacts

Abstract: A method for reducing an effect of a video artifact includes adjusting a phase of a second imaging device's video clock signal so that a phase of the second imaging device's video synchronization signal matches a phase of a first imaging device's video synchronization signal. An endoscopic system includes a first imaging device, a second imaging device, a light source, and a controller that reduces an artifact in an image produced by the first imaging device. In some embodiments, the first imaging device faces the light source.

Artifact Removal in Magnetoencephalogram Background Activity With Independent Component Analysis

Abstract: The aim of this study was to assess whether independent component analysis (ICA) could be valuable to remove power line noise, cardiac, and ocular artifacts from magnetoencephalogram (MEG) background activity. The MEGs were recorded from 11 subjects with a 148-channel whole-head magnetometer. We used a statistical criterion to estimate the number of independent components. Then, a robust ICA algorithm decomposed the MEG epochs and several methods were applied to detect those artifacts. The whole process had been previously tested on synthetic data. We found that the line noise components could be easily detected by their frequency spectrum. In addition, the ocular artifacts could be identified by their frequency characteristics and scalp topography. Moreover, the cardiac artifact was better recognized by its skewness value than by its kurtosis one. Finally, the MEG signals were compared before and after artifact rejection to evaluate our method.

Appearance of coherent artifact signals in femtosecond transient absorption spectroscopy in dependence on detector design

Abstract: The appearance of coherent artifact signals in transient absorption spectroscopy employing a spectrally integrated detection system is studied. The influence of the detection design on the shape of the observed signal is detailed and the experimentally very important case, in which the shape of the coherent artifact is strongly influenced by the presence of the sample itself, is considered – leading to the situation that the artifact signal cannot be accounted for by simple comparison of the kinetics obtained for the solvent only. Finally, an estimate of the relative contribution of the artifact to the overall transient absorption changes is presented facilitating the interpretation of short time transients in the presence of artifact contributions and allowing to estimate the excited state absorption cross-section for a known pump-intensity dependence of the artifact signal.

Noise Cancellation Signal Processing Method and Computer System for Improved Real-Time Electrocardiogram Artifact Correction During MRI Data Acquisition

Abstract: A system was developed for real-time electrocardiogram (ECG) analysis and artifact correction during magnetic resonance (MR) scanning, to improve patient monitoring and triggering of MR data acquisitions. Based on the assumption that artifact production by magnetic field gradient switching represents a linear time invariant process, a noise cancellation (NC) method is applied to ECG artifact linear prediction. This linear prediction is performed using a digital finite impulse response (FIR) matrix, that is computed employing ECG and gradient waveforms recorded during a training scan. The FIR filters are used during further scanning to predict artifacts by convolution of the gradient waveforms. Subtracting the artifacts from the raw ECG signal produces the correction with minimal delay. Validation of the system was performed both off-line, using prerecorded signals, and under actual examination conditions. The method is implemented using a specially designed Signal Analyzer and Event Controller (SAEC) computer and electronics. Real-time operation was demonstrated at 1 kHz with a delay of only 1 ms introduced by the processing. The system opens the possibility of automatic monitoring algorithms for electrophysiological signals in the MR environment

Real time ECG artifact removal for myoelectric prosthesis control.

Abstract: 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.

Removal of ocular artifacts from the EEG: a comparison between time-domain regression method and adaptive filtering method using simulated data

Abstract: We recently proposed an adaptive filtering (AF) method for removing ocular artifacts from EEG recordings. The method employs two parameters: the forgetting factor λ and the filter length M. In this paper, we first show that when λ = M = 1, the adaptive filtering method becomes equivalent to the widely used time-domain regression method. The role of λ (when less than one) is to deal with the possible non-stationary relationship between the reference EOG and the EOG component in the EEG. To demonstrate the role of M, a simulation study is carried out that quantitatively evaluates the accuracy of the adaptive filtering method under different conditions and comparing with the accuracy of the regression method. The results show that when there is a shape difference or a misalignment between the reference EOG and the EOG artifact in the EEG, the adaptive filtering method can be more accurate in recovering the true EEG by using an M larger than one (e.g. M = 2 or 3).

A post-reconstruction method to correct cupping artifacts in cone beam breast computed tomography

Abstract: In cone beam breast computed tomography (CT), scattered radiation leads to nonuniform biasing of CT numbers known as a cupping artifact. Besides being visual distractions, cupping artifacts appear as background nonuniformities, which impair efficient gray scale windowing and pose a problem in threshold based volume visualization/segmentation. To overcome this problem, we have developed a background nonuniformity correction method specifically designed for cone beam breast CT. With this technique, the cupping artifact is modeled as an additive background signal profile in the reconstructed breast images. Due to the largely circularly symmetric shape of a typical breast, the additive background signal profile was also assumed to be circularly symmetric. The radial variation of the background signals were estimated by measuring the spatial variation of adipose tissue signals in front view breast images. To extract adipose tissue signals in an automated manner, a signal sampling scheme in polar coordinates and a background trend fitting algorithm were implemented. The background fits compared with targeted adipose tissue signal value (constant throughout the breast volume) to get an additive correction value for each tissue voxel. To test the accuracy, we applied the technique to cone beam CT images of mastectomy specimens. After correction, the images demonstrated significantly improved signal uniformity in both front and side view slices. The reduction of both intra-slice and inter-slice variations in adipose tissue CT numbers supported our observations.

Artifacts and pitfalls in MR imaging of the pelvis

Abstract: Artifacts are intimately intertwined with MRI. For the practicing radiologist, effective supervision, troubleshooting, and interpretation of diagnostic MR studies require a solid knowledge of the pertinent artifacts. This article seeks to familiarize the reader with commonly encountered artifacts and pitfalls in pelvic imaging, the mechanism behind their generation, and methods of minimizing their negative impact or maximizing their diagnostic yield. It also serves as an exciting tool to learn many aspects of basic and advanced MR physics. Artifacts are categorized into patient- and sequence-related artifacts. Various manifestations of motion and vascular artifacts, susceptibility, altered tissue contrast, blurring, chemical shift artifact, volume averaging, and gadolinium (Gd) pseudolayering are explained, along with their proposed remedies.

Low Variance Adaptive Filter for Cancelling Motion Artifact in Wearable Photoplethysmogram Sensor Signals

Abstract: The photoplethysmogram (PPG) is an extremely useful wearable sensing medical diagnostic tool. However, the PPG signal becomes highly corrupted and unusable when the sensor wearer is in motion. This paper investigates how confidently Widrow's Adaptive Noise Cancellation can eliminate motion artifact and recover a motion corrupted PPG signal for a wearer engaged in jogging motions. It has previously been shown that Widrow's Adaptive Noise Cancellation can recover a motion corrupted PPG signal for certain data sets by using a collocated accelerometer to measure the corrupting motion. However, wearer motion is band limited, and provides little information for estimating motion-to-PPG noise transfer dynamics. This means that, without proper care, recovery results can be unreliable. In the present work, both Finite Impulse Response (FIR) and Laguerre series black box transfer dynamics models are evaluated for how confidently they can be identified. Model confidence is quantified in terms of variance of the transfer dynamics estimate at the motion frequencies. For typical jogging motion, it is found that standard deviation of the FIR model transfer dynamics is 30% of the mean value at the motion input frequency. The standard deviation of the Laguerre model transfer dynamics is only 1%. Time domain data shows how a Laguerre model outperforms a FIR model in accordance with the computed model variance.

Capture verification using an evoked response reference

Abstract: A method and system for verifying capture in the heart involves the use of pacing artifact templates. One or more pacing artifact templates characterizing a post pace artifact signal associated with a particular pace voltage or range of voltages are provided. A pacing artifact template is canceled from a cardiac signal sensed following a pacing pulse. Capture is detected by comparing the pacing artifact canceled cardiac signal to an evoked response reference. Fusion/pseudofusion detection involves determining a correlation between a captured response template and a sensed cardiac signal.

Recognizing and Interpreting Artifacts and Pitfalls in MR Imaging of the Breast

Abstract: Magnetic resonance (MR) imaging of the breast has evolved into an important adjunctive tool in breast imaging with multiple and ever-increasing indications for its use. As with other types of MR imaging, there are a number of technical artifacts and pitfalls that can potentially limit interpretation of the images by masking or simulating disease. Because of the coils and computer-aided detection software specific to breast MR imaging, there are additional technical considerations that are unique to this type of MR imaging. Motion and misregistration artifacts, wraparound artifact, susceptibility artifact, poor fat saturation, lack of contrast material, and poor timing of the contrast material bolus are some of the artifacts and pitfalls that can make interpretation of breast MR images challenging and lead to misdiagnosis. Other important considerations in proper interpretation of breast MR images include acquisition of a sufficient medical history, knowledge of benign and abnormal lesion enhancement, morp...

Method and apparatus for automatic visual artifact analysis and artifact reduction

Abstract: A system and method for correcting artifacts present in video frames is disclosed. The system includes a decision module (110) and an artifact correction unit (120). The system receives artifact metrics (112) and artifact maps (122) corresponding to artifacts present in video frames and prioritizes the video frames to be corrected by considering the type and severity of the artifacts and the content of the video frames (113). Subsequent to prioritizing the video frames, the system corrects artifacts of selected frames by adjusting compressed domain parameters (126), such as quantization parameters and mode decisions. An encoder (130) compresses the video frames according to the adjusted compressed domain parameter to automatically provide a video stream with a lower incidence and severity of artifacts.