Showing papers on "Artifact (error) published in 2006"

Advanced Methods And Tools for ECG Data Analysis

Abstract: This cutting-edge resource provides you with a practical and theoretical understanding of state-of-the-art techniques for electrocardiogram (ECG) data analysis. Placing an emphasis on the fundamentals of signal etiology, acquisition, data selection, and testing, this comprehensive volume presents guidelines to help you design, implement, and evaluate algorithms used for the analysis of ECG and related data. Additionally, explanations of open source software and related databases for signal processing are given. The book focuses on the modeling, classification, and interpretation of features derived from advanced signal processing and artificial intelligence techniques. Key topics covered include physiological origin, hardware acquisition and filtering, time-frequency quantification of the ECG and derived signals (including heart rate variability and respiration), analysis of noise and artifact, models for ECG and RR interval processes, linear and nonlinear filtering techniques, and adaptive algorithms such as neural networks. Much of the book is devoted to deriving robust, clinically meaningful parameters such as the QRS axis, QT-interval, the ST-level, and T-wave alternan metrics. Methods for applying these metrics to clinical classification are also discussed, together with supervised and unsupervised classification techniques. Including over 190 illustrations, the book offers you a solid grounding in the relevant basics of physiology, data acquisition and database design, and addresses the practical issues of improving existing data analysis methods and developing new applications.

Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram

Abstract: The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique. This method is demonstrated on a synthetic data set. The method outperformed a low-pass filter with different cutoff frequencies and an independent component analysis (ICA)-based technique for muscle artifact removal. In addition, the method is applied on a real ictal EEG recording contaminated with muscle artifacts. The proposed method removed successfully the muscle artifact without altering the recorded underlying ictal activity

Motion artifact reduction in photoplethysmography using independent component analysis

Abstract: Removing the motion artifacts from measured photoplethysmography (PPG) signals is one of the important issues to be tackled for the accurate measurement of arterial oxygen saturation during movement. In this paper, the motion artifacts were reduced by exploiting the quasi-periodicity of the PPG signal and the independence between the PPG and the motion artifact signals. The combination of independent component analysis and block interleaving with low-pass filtering can reduce the motion artifacts under the condition of general dual-wavelength measurement. Experiments with synthetic and real data were performed to demonstrate the efficacy of the proposed algorithm.

Imaging artifacts at 3.0T.

Abstract: Clinical MRI at a field strength of 3.0T is finding increasing use. However, along with the advantages of 3.0T, such as increased SNR, there can be drawbacks, including increased levels of imaging artifacts. Although every imaging artifact observed at 3.0T can also be present at 1.5T, the intensity level is often higher at 3.0T and thus the artifact is more objectionable. This review describes some of the imaging artifacts that are commonly observed with 3.0T imaging, and their root causes. When possible, countermeasures that reduce the artifact level are described.

Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach.

Abstract: Independent component analysis (ICA) proves to be effective in the removing the ocular artifact from electroencephalogram recordings (EEG). While using ICA in ocular artifact correction, a crucial step is to correctly identify the artifact components among the decomposed independent components. In most previous works, this step of selecting the artifact components was manually implemented, which is time consuming and inconvenient when dealing with a large amount of EEG data. We present a new method which automatically selects the eye blink artifact components based on the pattern of their scalp topographies, which can be exemplified as a template matching approach. The feasibility of using a fixed template for singling out the eye blink component after ICA decomposition was validated by an experiment in which 18 subjects among the 21 subjects involved exhibited a highly consistent pattern of eye blink scalp topographies. Since only the spatial feature is employed for singling out the eye blink component, the proposed method is very efficient and easy to implement. Objective evaluation of the real results shows that the proposed algorithm can remove the eye blink artifact from the EEG while causing little distortion to the underlying brain activities.

Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform

Abstract: The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Eye-blinks and movement of the eyeballs produce electrical signals that are collectively known as Ocular Artifacts (OA). These are of the order of milli-volts and they contaminate the EEG signals which are of the order of micro-volts. The frequency range of EEG signal is 0 to 64 Hz and the OA occur within the range of 0 to 16 Hz. If the wavelet based EOG correction algorithm is applied to the entire length of the EEG signal, it results in thresholding of both low frequency and high frequency components even in the non-OA zones. This leads to considerable loss of valuable background EEG activity. Though the detection of OA zones can be done by visual inspection, the OA time zones need to be given as input to the EOG correction procedure, which is a laborious process. Hence there is a need for automatic detection of artifact zones. This paper discusses a method to automatically identify slow varying OA zones and applying wavelet based adaptive thresholding algorithm only to the identified OA zones, which avoids the removal of background EEG information. Adaptive thresholding applied only to the OA zone does not affect the low frequency components in the non-OA zones and also preserves the shape (waveform) of the EEG signal in non- artifact zones which is of very much importance in clinical diagnosis.

Image artifact in dental cone-beam CT

Abstract: Purpose The purpose of this study was to investigate the appearance and possible cause of an artifact seen in limited-volume cone-beam CT imaging. Methods A water-filled plastic cylinder was used as a phantom of the head. A test object was constructed as a bone-equivalent phantom to be imaged. The test object was variously positioned at the center of the phantom and near its margins. CT images of the test object were acquired using a 3DX Accuitomo system. Results In slice images with the test object positioned near the margin of the phantom, arch-shaped defects or deformities were observed on the side of the object. There was a negative correlation between the artifact and the CT value of the object. The artifact was larger in images scanned with a higher voltage. Conclusion The probability that this artifact is caused by halation from the image intensifier (II) system is suggested.

ECG denoising based on the empirical mode decomposition.

Abstract: The electrocardiogram (ECG) has been widely used for diagnosis purposes of heart diseases. Good quality ECG are utilized by the physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. One prominent artifact is the high frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes. Noise severely limits the utility of the recorded ECG and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG denoising. In this paper, we proposed a new ECG denoising method based on the recently developed Empirical Mode Decomposition (EMD). The proposed EMD-based method is able to remove high frequency noise with minimum signal distortion. The method is validated through experiments on the MIT-BIH database. Both quantitative and qualitative results are given. The results show that the proposed method provides very good results for denoising.

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

Abstract: 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.

A model-based objective evaluation of eye movement correction in EEG recordings

Abstract: We present a method to quantitatively and objectively compare algorithms for correction of eye movement artifacts in a simulated ongoing electroencephalographic signal (EEG). A realistic model of the human head is used, together with eye tracker data, to generate a data set in which potentials of ocular and cerebral origin are simulated. This approach bypasses the common problem of brain-potential contaminated electro-oculographic signals (EOGs), when monitoring or simulating eye movements. The data are simulated for five different EEG electrode configurations combined with four different EOG electrode configurations. In order to objectively compare correction performance for six algorithms, listed in Table III, we determine the signal to noise ratio of the EEG before and after artifact correction. A score indicating correction performance is derived, and for each EEG configuration the optimal correction algorithm and the optimal number of EOG electrodes are determined. In general, the second-order blind identification correction algorithm in combination with 6 EOG electrodes performs best for all EEG configurations evaluated on the simulated data.

On Semi-Blind Source Separation Using Spatial Constraints With Applications in EEG Analysis

Abstract: Blind source separation (BSS) techniques, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing applications, including the analysis of multichannel electroencephalogram (EEG) and magnetoencephalogram (MEG) signals. These methods estimate a set of sources from the observed data, which reflect the underlying physiological signal generating and mixing processes, noise and artifacts. In practice, BSS methods are often applied in the context of additional information and expectations regarding the spatial or temporal characteristics of some sources of interest, whose identification requires complicated post-hoc analysis or, more commonly, manual selection by human experts. An alternative would be to incorporate any available prior knowledge about the source signals or locations into a semi-blind source separation (SBSS) approach, effectively by imposing temporal or spatial constraints on the underlying source mixture model. This work is concerned with biomedical applications of SBSS using spatial constraints, particularly for artifact removal and source tracking in EEG analysis, and provides definitions of different types of spatial constraint along with general guidelines on how these can be implemented in conjunction with conventional BSS methods

The Problem of Artifacts in Patient Monitor Data During Surgery: A Clinical and Methodological Review

Abstract: Artifacts are a significant problem affecting the accurate display of information during surgery. They are also a source of false alarms. A secondary problem is the inadvertent recording of artifactual and inaccurate information in automated record keeping systems. Though most of the currently available patient monitors use techniques to minimize the effect of artifacts, their success is limited. We reviewed the problem of artifacts affecting patient monitor data during surgical cases. Methods adopted by currently marketed patient monitors to eliminate and minimize artifacts due to technical and environmental factors are reviewed and discussed. Also discussed are promising artifact detection and correction methods that are being investigated. These might be used to detect and eliminate artifacts with improved accuracy and specificity.

Monitoring physiological signals during external electrical stimulation

Abstract: Electrodes and circuitry for monitoring and stimulating the exterior of the human body, comprising delivering stimulation pulses to stimulation electrodes applied to the exterior of the body, detecting an electrical potential at monitoring electrodes applied to the exterior of the body, positioning at least a first and second monitoring electrode at locations at which an electrical artifact caused by the electrical stimulation pulses is substantially cancelled in a signal formed from the electrical potentials detected at the first and second monitoring electrodes.

Detection of artifacts in bioelectric signals

Abstract: The invention relates to a method and apparatus for detecting artifacts in a bioelectric signal, especially in a frontal EEG signal. In order to accomplish an uncomplicated mechanism for detecting artifacts in clinical applications, an impedance signal is measured through a first electrode set attached to the skin surface in a measurement area of a patient's body, the impedance signal being indicative of the impedance of the signal path formed between individual electrodes of the set. Simultaneously with the impedance measurement, a bioelectric signal is acquired through a second electrode set also attached to the skin surface of the measurement area, and the time periods are determined during which the impedance signal fulfills at least one predetermined criterion indicative of the presence of artifact in the bioelectric signal. In one embodiment, the first and second electrode sets are formed by a common set of two electrodes.

Nanometer-Level Comparison of Three Spindle Error Motion Separation Techniques

Abstract: This work demonstrates the state of the art capabilities of three error separation techniques for nanometer-level measurement of precision spindles and rotationally-symmetric artifacts. Donaldson reversal is compared to a multi-probe and a multi-step technique using a series of measurements carried out on a precision aerostatic spindle with a lapped spherical artifact. The results indicate that subnanometer features in both spindle error motion and artifact form are reliably resolved by all three techniques. Furthermore, the numerical error values agree to better than one nanometer. The paper discusses several issues that must be considered when planning spindle or artifact measurements at the nanometer level.

Artifact mitigation in three-dimensional imaging

Abstract: Accuracy of a three-dimensional imaging system is improved through the use of model-based calibration and lookup tables to resolve distance according to, e.g., x-displacement, y-displacement, or image disparity data. In an embodiment, the lookup table(s) stores localized parameterization data used to calculate calibrated results.

New algorithms for ring artifact removal

Abstract: This paper describes a set of algorithms that enable virtually complete ring artifact removal from tomographic imagery with minimal to negligible contamination of the underlying data. These procedures were created specifically to deal with data as acquired at the University of Texas high-resolution X-ray CT facility, but are likely to be applicable in other settings as well. In most cases corrections are optimally applied to sinogram data before reconstruction, but a variant is developed for correcting already-reconstructed images. The algorithms make particular use of repetitive aspects of the artifact across images to improve behavior. However, fully utilizing this functionality requires processing entire data sets simultaneously, rather than one image at a time. A number of parameters may be adjusted to optimize results for particular data sets.

Geometric subspace methods and time-delay embedding for EEG artifact removal and classification

Abstract: Generalized singular-value decomposition is used to separate multichannel electroencephalogram (EEG) into components found by optimizing a signal-to-noise quotient. These components are used to filter out artifacts. Short-time principal components analysis of time-delay embedded EEG is used to represent windowed EEG data to classify EEG according to which mental task is being performed. Examples are presented of the filtering of various artifacts and results are shown of classification of EEG from five mental tasks using committees of decision trees.

Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation

Abstract: The electrocardiogram (ECG) artifact is a major noise contaminating the myoelectric control signals when using shoulder disarticulation prosthesis This is an even more significant problem with targeted muscle reinnervation to develop additional myoelectric sites for improved prosthesis control in a bilateral amputee at shoulder disarticulation level This study aims at removal of ECG artifacts from the myoelectric prosthesis control signals produced from targeted muscle reinnervation Three ECG artifact removal methods based on template subtracting, wavelet thresholding and adaptive filtering were investigated, respectively Surface EMG signals were recorded from the reinnervated pectoralis muscles of the amputee As a key parameter for clinical myoelectric prosthesis control, the amplitude measurement of the signal was used as a performance indicator to evaluate the proposed methods The feasibility of the different methods for clinical application was also investigated with consideration of the clinical speed requirements and memory limitations of commercial prosthesis controllers

Bipolar deep brain stimulation permits routine EKG, EEG, and polysomnography

Abstract: As the population of patients treated with deep brain stimulation (DBS) grows and the patients age, more will require routine or emergent electrophysiologic tests. DBS artifact may render these uninterpretable, whereas stopping DBS may release symptoms that confound evaluation. The authors find that monopolar, but not bipolar, stimulation produces significant artifact during EKG, EEG, and polysomnography.

Characterization of the susceptibility artifact around a prostate brachytherapy seed in MRI.

Abstract: Magnetic distortions surrounding a typical brachytherapy seed (IMC6711, OncoSeed) within a clinical magnetic resonance imager were modeled for a number of different seed orientations with respect to the main magnetic field. From these distortion maps, simulated images were produced. The simulated images were then compared to images experimentally acquired using a spin echo technique on a Philips 1.5 T magnetic resonance imaging scanner. The modeled images were found to conform very well to those acquired experimentally, thus allowing one to establish where the seed is positioned within the complex image distortion patterns. The artifact patterns were dependent on the orientation of the seed with the main magnetic field, as well as the direction of the read encode gradient. While all imaging schemes which employ a unidirectional linear read encode trajectory should produce the artifacts modeled in this article, sequences other than spin echo may produce additional artifacts. Gradient echo and steady-state free precession imaging techniques were also performed on the seed for comparison.

Method for the automatic simultaneous synchronization, calibration and qualification of a non-contact probe

Abstract: The present invention relates to an improved method for the simultaneous calibration and qualification of a non-contact probe on a localizer using a single artifact, in which non-contact probe readings and localizer readings are synchronised using parameters determined simultaneously with calibration and qualification. The invention also relates to a non-contact probe and other devices, and a computer program for performing the invention.