Motion Artifact Noise Research Articles

Quality controlled compression technique for Photoplethysmogram monitoring applications

Computerized acquisition and analysis of Photoplethysmogram (PPG) can provide vital information on various cardiovascular functions. In this paper, we introduce a quality controlled PPG compression technique using principal component analysis to keep the local and global distortion of the reconstructed data within pre-specified limits. To achieve this, optimum number of eigenvectors (EV) and principal components (PC) along with their quantization level (Q) were selected for compression. The technique was validated with pre-recorded 2 min and 10 min PPG data collected from 35 healthy volunteers (N) and 35 cardiovascular patients (CVP) under fully resting condition. At limiting PRD of 5% and absolute error of 8%, for N group (CVP group) an average compression ratio (CR), PRD and MAE of 5.65 (6.76), 2.41% (3.33%) and 6.1% (7.3%) were obtained respectively. Relaxing the control criteria up to clinically acceptable limits, a CR of 13.28 for N and 13.31 for CVP group were achieved. Most of the clinical features had an average deviation of less that 6% from their original values. The method yielded lower CR, PRDN and MAE with noisy (motion artifact and Gaussian noise) PPG. With two different stress levels, the compression parameters were found to vary. The reconstruction results were clinically validated by experts. A successful real-time data streaming in computer proved the usefulness of the technique for continuous monitoring applications.

Motion Artifact Removal for PPG Signals based on Accurate Fundamental Frequency Estimation and Notch Filtering.

This study proposes a new method for motion artifact (MA) removal in Photoplethysmography (PPG) signal that combines accurate heart rate (HR) frequency estimation and notch filtering. The method applies LMS-Newton adaptive filtering to reduce motion artifact noise and uses a novel HR correction stage for accurate HR frequency estimation. Notch filters are used to recover clean PPG signal from HR frequency and second harmonic frequency of PPG. On a widely used dataset of 12 recordings, our method achieves an averaged HR error of 0. 92bpm and a Pearson correlation of 0.997. Experimental results further show that our method can recover the PPG waveform with clear dicrotic peaks even for strongly MA-corrupted PPG signal.

Electrocardiogram noise cancellation using wavelet transform

This paper presenting a new electrocardiogram (ECG) noise removal based on wavelet transform technique. The system involves two stages filtration, which denoising process starts with ECG thresholds with several threshold methods. The residual signal will feed to the second stage filtration using the high/low wavelet based filter. Wavelet transform methods capable to counter the unpredictability of motion artifact noise which produce by physical movement. Wavelets may reduce the motion artifact effects since it has the capability to filter both high and low frequency signals and produce good results for both stationary and non-stationary signals. The performance of the wavelet arrangement is shown by using ECG signals taken from the MIT-BIH ECG database.

Single Scale CWT Algorithm for ECG Beat Detection for a Portable Monitoring System

This paper introduces an algorithm for ECG beat detection for portable monitoring systems using the continuous wavelet transform technique. It uses the Mexican-Hat wavelet as an analyzing wavelet. The central frequency of the corresponding band-pass filter frequency response is chosen to be 32 Hz after performing scale analysis. It relies on the adaptive threshold technique to minimize the number of false positive beats and therefore guarantee robustness against high motion artifact noise levels, it also relies on search back to avoid missing real heart beats and hence obtain a high sensitivity. The algorithm was implemented using MATLAB and has a sensitivity Se = 99.37% and a positive predictivity P+ = 99.83% with the MIT-BIH arrhythmia database. Furthermore, in order to test the performance of the algorithm against motion artifact noise, a noise stress test was performed by adding motion artifact noise to the ECG records of the same database at various signal to noise ratio values. The results of the noise stress test were benchmarked against some existing algorithms in the literature such as Pan and Tompkins and Romero.

IJARCCE - Computer and Communication Engineering

2 Abstract: This paper aims at studying the various techniques and algorithms available in literature for the cancellation of various types of artifacts occurring out of the noise present in the Electrocardiogram (ECG) signal. It further aims at the enhancement of the ECG signal for proper monitoring and diagnosis of the patient. In the process of the transfer of the signal information from the location of the patient to a remote location, certain kind of noise is introduced in the signal that results in the disturbance as well as interference of the signal. Hence, the information cannot be predicted accurately. Thus, it is important to remove the resulting noise and the artifacts in order to enhance the ECG signal for proper treatment of the patient. During the acquisition of ECG signal in clinical environment, it can encounter numerous types of artifacts. Power line interference, muscle artifact, motion artifact, respiration movements, baseline wander and instrumentation noise are the ones of primary interest. As a result, there is degradation in the quality of the signal. The other effects includes frequency resolution, the morphology of ECG signal is strongly affected which provides valuable information about heart diseases or arrhythmias. Hence, it is quite essential to reduce interferences in ECG signal and enhance the reliability as well as accuracy for better diagnosis. The acquisition of ECG signal can be traced back in 1887 when Waller recorded the first ECG signal by capillary electrometer. An improvement in this method was seen with the introduction of string galvanometer and this was credited to Einthoven. Electronic amplifiers replaced the string-galvanometer that lead to the development of direct writing recorder or paper plotter to get ECG strips. In modern times, the acquisition of ECG is carried out by the placement of electrodes at standardized locations on the surface of the skin of the patient. It is a non-invasive technique. The cardiac health of human heart can be well predicted from the heart rate and the ECG signal. Cardiac arrhythmia is a result of the rhythm or change in the morphological pattern of the heart. Basically, a disorder in heart rate causes cardiac arrhythmia. By the analysis of the ECG waveform, these arrhythmias can be detected as well as diagnosed. The characteristics of the P-QRS-T-U waveform that is the duration and amplitude of the wave can serve as the basis to diagnose and extract useful information about the nature of the disease.

Automatic measurements of arterial input and venous output functions on cerebral computed tomography perfusion images: A preliminary study

BackgroundThe current automatic techniques for measuring arterial input function (AIF) and venous output (VOF) on cerebral computed tomography perfusion images are prone to motion artifact and random noise, and their failure rates vary between 10% and 65%. We developed a new automatic technique to overcome these problems. MethodsA principle axis transformation was applied to perfusion images to correct for translational and rotational motion artifacts. Bone voxels and neighboring voxels were removed from the perfusion images. Only brain voxels were included in the AIF and VOF measurement procedures. The selection criteria, such as large area under the concentration–time curve, early arrival of contrast agents, and narrow effective width, were used to select appropriate arterial and venous voxels for the AIF and VOF measurements. The proposed automatic technique was tested in 20 patients with unilateral cerebral arterial stenosis. The results of the proposed technique were compared to the results obtained by manual measurements and commercially available automatic selection software. ResultsThe AIFs and VOFs were successfully measured using the proposed automatic technique in all 20 patients. The curve shapes, including the area under the concentration–time curve, peak concentration, time to peak, and effective width of the automatically measured AIFs or VOFs were comparable to that were measured manually. ConclusionThe proposed automatic measurement technique successfully overcomes the motion artifact and random noise problems encountered in measuring AIF and VOF. It can be easily integrated into software for the automatic calculation of cerebral blood volume and flow.

CANCELLATION OF MOTION ARTIFACT NOISE AND POWER LINE INTERFERENCE IN ECG USING ADAPTIVE FILTERS

Background: The electrocardiogram(ECG) has the considerable diagnostic significance, and applications of ECG monitoring are diverse and in wide use. Noises that commonly disturb the basic electrocardiogram are power line interference(PLI), instrumentation noise, external electromagnetic field interference, noise due to random body movements and respiration movements. These noises can be classified according to their frequency content. It is essential to reduce these disturbances in ECG signal to improve accuracy and reliability. The bandwidth of the noise overlaps that of wanted signals, so that simple filtering cannot sufficiently enhance the signal to noise ratio. It is difficult to apply filters with fixed filter co-efficients to reduce these noise. Adaptive filter technique is required to overcome this problem as the filter coefficients can be varied to track the dynamic variations of the signals. Adaptive filter based on the least mean square (LMS) algorithm and recursive least squares (RLS) algorithm are applied to noisy ECG to reduce 50 Hz power line noise and motion artifact noise. Method: ECG signal is taken from physionet database. A ECG signal (without noise) was mixed with constant 0.1 mVp-p 50 Hz interference and motion artifact noise processed with Adaptive filter based on the least mean square (LMS) algorithm and recursive least squares (RLS) algorithm. Simulation results are also shown. Performance of filters are analyzed based on SNR and MSE.

Motion artifact and speckle noise reduction in polarization sensitive optical coherence tomography by retinal tracking

We present a novel polarization sensitive optical coherence tomography (PS-OCT) system with an integrated retinal tracker. The tracking operates at up to 60 Hz, correcting PS-OCT scanning positions during the acquisition to avoid artifacts caused by eye motion. To demonstrate the practical performance of the system, we imaged several healthy volunteers and patients with AMD both with B-scan repetitions for frame averaging and with 3D raster scans. Under large retinal motions with up to 1 mm amplitude at 0.5 ~a few Hz frequency range, motion artifact suppression in the PS-OCT images as well as standard deviation noise reduction in the frame averaged retardation images are presented.

Multimodal Physical Activity Recognition by Fusing Temporal and Cepstral Information

A physical activity (PA) recognition algorithm for a wearable wireless sensor network using both ambulatory electrocardiogram (ECG) and accelerometer signals is proposed. First, in the time domain, the cardiac activity mean and the motion artifact noise of the ECG signal are modeled by a Hermite polynomial expansion and principal component analysis, respectively. A set of time domain accelerometer features is also extracted. A support vector machine (SVM) is employed for supervised classification using these time domain features. Second, motivated by their potential for handling convolutional noise, cepstral features extracted from ECG and accelerometer signals based on a frame level analysis are modeled using Gaussian mixture models (GMMs). Third, to reduce the dimension of the tri-axial accelerometer cepstral features which are concatenated and fused at the feature level, heteroscedastic linear discriminant analysis is performed. Finally, to improve the overall recognition performance, fusion of the multimodal (ECG and accelerometer) and multidomain (time domain SVM and cepstral domain GMM) subsystems at the score level is performed. The classification accuracy ranges from 79.3% to 97.3% for various testing scenarios and outperforms the state-of-the-art single accelerometer based PA recognition system by over 24% relative error reduction on our nine-category PA database.

HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain.

Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.

Tonometric Arterial Pulse Sensor With Noise Cancellation

Arterial tonometry provides for the continuous and noninvasive recording of the arterial pressure waveform. However, tonometers are affected by motion artifact that degrades the signal. An arterial tonometer was constructed using two piezoelectric transducers centered within a solid base. In two subjects, one transducer was positioned over the radial pulse (p) and the other was positioned on the wrist not overlying the pulse (n). The presence of induced motion artifact and any noise was removed after signal digitization by noise cancellation. Besides fixed weighting, two adaptive algorithms were used for cancellation-LMS and differential steepest descent (DSD). Criteria were developed for comparison of the adaptive techniques. The best fixed weighting for noise cancellation was w=0.6. For fixed-weighting, LMS, and DSD, the mean peak-to-peak errors were 1.22+/-0.54, 1.18+/-0.30, and 1.16+/-0.23 V, respectively, and the mean point-to-point errors were 15.86+/-3.15, 11.40+/-1.96, and 10.13+/-1.25 V, respectively. Noise cancellation using a common-mode reference input substantially reduces motion artifact and other noise from the acquired tonometric arterial pulse signal. Adaptive weighting provides better cancellation than fixed weighting, likely because the mechanical gain at the transducer-skin interface is time-varying.

Electrocardiogram signals de-noising using lifting-based discrete wavelet transform

This paper introduces an effective technique for the denoising of electrocardiogram (ECG) signals corrupted by nonstationary noises. The technique is based on a second generation wavelet transform and level-dependent threshold estimator. Here, wavelet coefficients of ECG signals were obtained with lifting-based wavelet filters. A lifting scheme is used to construct second-generation wavelets and is an alternative and faster algorithm for a classical wavelet transform. The overall denoising performance of our proposed method is considered in relation to several measuring parameters, including types of wavelet filters (Haar, Daubechies 4 (DB4), Daubechies 6 (DB6), Filter(9-7), and Cubic B-splines), thresholding method, and decomposition depth. Three different kinds of noise were considered in this work: muscle artifact noise, electrode motion artifact noise, and white noise. Global performance is evaluated by means of the signal-to-noise ratio and visual inspection. Numerical results comparing the performance of the proposed method with that of nonlinear filtering techniques (median filter) are given. The results demonstrate consistently superior denoising performance of the proposed method over median filtering.

Motion and Ballistocardiogram Artifact Removal for Interleaved Recording of EEG and EPs during MRI

Artifactsgenerated by motion (e.g., ballistocardiac) of the head inside a high magnetic field corrupt recordings of EEG and EPs. This paper introduces a method for motion artifact cancellation. This method is based on adaptive filtering and takes advantage of piezoelectric motion sensor information to estimate the motion artifact noise. This filter estimates the mapping between motion sensor and EEG space, subtracting the motion-related noise from the raw EEG signal. Due to possible subject motion and changes in electrode impedance, a time-varying mapping of the motion versus EEG is required. We show that this filter is capable of removing both ballistocardiogram and gross motion artifacts, restoring EEG alpha waves (8–13 Hz), and visual evoked potentials (VEPs). This adaptive filter outperforms the simple band-pass filter for alpha detection because it is also capable of reducing noise within the frequency band of interest. In addition, this filter also removes the transient responses normally visible in the EEG window after echo planar image acquisition, observed during interleaved EEG/fMRI recordings. Our adaptive filter approach can be implemented in real-time to allow for continuous monitoring of EEG and fMRI during clinical and cognitive studies.

4703274 Arrangement of RF coils and related method for magnetic resonance imaging of a selected inner-volume

Shaped RF field distributions from separate "surface" coils overlap to define a limited inner-volume deep within a human body or other object under examination. A spin echo MRI signal is effectively elicited only from such limited inner-volume so as to permit conventional MRI signal processing (e.g., utilizing Fourier Transformations) to derive and display magnetic resonance images of desired cross-sections of the limited inner-volume thus avoiding possible motion artifact and/or other potential noise sources located elsewhere in the object. A special receiver coil decoupling circuit is used to automatically increase its resonant frequency during RF transmit times.