Motion Artifact Removal Research Articles

Motion artifact reduction in photoplethysmogram signals based on Periodic Component Factorization.

Continuous blood pressure measurement based on pulse transit time (PTT) is a deeply research topic over recent decades. Advanced algorithms have been proposed by scholars to give satisfactory estimation in stationary position. Nevertheless, pulse transit time (PTT) is shown to be strongly affected by hand movement and the estimation of blood pressure is no longer accurate under strenuous exercise. Because of this, a novel algorithm called Periodic Component Factorization (PCF), which is an extension of Independent Component Analysis (ICA), for better removal of motion artifact (MA) from photoplethysmography (PPG) signals is proposed in this paper. Compared to FastICA algorithm based on nongaussianity such as kurtosis and skewness, PCF is able to extract dependent source components from noisy signals when the PPG signal shows quasi-periodicity or periodicity. This newly proposed algorithm undoubtedly shows its practicality and effectiveness in removing motion artifact of PPG signals.

Robust motion artefact resistant circuit for calculation of Mean Arterial Pressure from pulse transit time.

Cuff-less and non-invasive methods of Blood Pressure (BP) monitoring have faced a lot of challenges like stability, noise, motion artefact and requirement for calibration. These factors are the major reasons why such devices do not get approval from the medical community easily. One such method is calculating Blood Pressure indirectly from pulse transit time (PTT) obtained from electrocardiogram (ECG) and Photoplethysmogram (PPG). In this paper we have proposed two novel analog signal conditioning circuits for ECG and PPG that increase stability, remove motion artefacts, remove the sinusoidal wavering of the ECG baseline due to respiration and provide consistent digital pulses corresponding to blood pulses/heart-beat. We have combined these two systems to obtain the PTT and then correlated it with the Mean Arterial Pressure (MAP). The aim was to perform major part of the processing in analog domain to decrease processing load over microcontroller so as to reduce cost and make it simple and robust. We have found from our experiments that the proposed circuits can calculate the Heart Rate (HR) with a maximum error of ~3.0% and MAP with a maximum error of ~2.4% at rest and ~4.6% in motion.

Considerations of handheld respiratory rate estimation via a stabilized Video Magnification approach.

Respiratory rate can be a vital indicator of illness; however, tracking this is a non-trivial process. Phase-based Eulerian Video Magnification (EVM) is an exciting spatiotemporal video processing approach able to reveal subtle breathing motions within video sequences; however, its results are variant to large motions and camera blur. In the case of camera motion, a compensation strategy of stabilizing (without smoothing) the video has the may reduce estimation error in handheld cases. This work explores the extent of removing motion artefacts and its impact on identifying subtle breathing motions. Tests across six indoor scenes show a reduction mean breathing estimate error for 4 of 6 cases and highlights the sensitivity of this approach to unwanted body movements. The results of this project suggest the plausibility that non-smoothing video amplification processes can be an effective method to track breathing motion and that implementing correction techniques which may allow a smartphone to provide a compact, non-invasive, online breathing monitor.

Performance Investigation of a Wearable Distributed-Deflection Sensor in Arterial Pulse Waveform Measurement

This paper presents a performance investigation of a wearable distributed-deflection sensor in arterial pulse waveform measurement. Built on a flexible substrate, the sensor entails a polymer microstructure embedded with an electrolyte-enabled resistive transducer array. By pressing the sensor against an artery with hold-down pressure exerted by two fingers, the pulse signal from the artery deflects the microstructure and registers as a resistance change by the transducer at the artery site. The radial and carotid pulse signals of five subjects are recorded via the sensor. Related signal-processing algorithms are written in Matlab to remove motion artifacts from a recorded pulse signal, extract its key tonometric parameters, and calculate the Pulse Wave Velocity (PWV) and radial and carotid Augmentation Index (AI). Whereas the tonometric parameters of the measured radial and carotid pulse waveforms on each subject are consistent with the related findings in the literature, the difference in tonometric parameters among the subjects also reveals physiological significance. The measured PWV and radial and carotid AI of the subjects show good agreement with how they should vary with age, gender and hypertension. Finally, the effect of the hold-down pressure on a measured pulse signal and the repeatability of the sensor are examined.

Motion artefact removals for wearable ECG using stationary wavelet transform.

Wearable Electrocardiogram (ECG) is attracting much attention in daily healthcare applications. From the viewpoint of long-term use, it is desired that the electrodes are non-contact with the human body. In this study, the authors propose an algorithm using the stationary wavelet transform (SWT) to remove motion artefact superimposed on ECG signal when using non-contact capacitively coupling electrodes. The authors evaluate the effect on motion artefact removal of this algorithm by applying it to various ECG signals with motion artefacts superimposed. As a result, the correlation coefficients of ECG signals with respect to the clean ones have been improved from 0.71 to 0.88 on median before and after motion artefact removal, which demonstrates the validity of the proposed SWT-based algorithm.

Novel tailoring algorithm for abrupt motion artifact removal in photoplethysmogram signals.

Photoplethysmogram (PPG) signals are widely used for wearable electronic devices nowadays. The PPG signal is extremely sensitive to the motion artifacts (MAs) caused by the subject's movement. The detection and removal of such MAs remains a difficult problem. Due to the complicated MA signal waveforms, none of the existing techniques can lead to satisfactory results. In this paper, a new framework to identify and tailor the abrupt MAs in PPG is proposed, which consists of feature extraction, change-point detection, and MA removal. In order to achieve the optimal performance, a data-dependent frame-size determination mechanism is employed. Experiments for the heart-beat-rate-measurement application have been conducted to demonstrate the effectiveness of our proposed method, by a correct detection rate of MAs at 98% and the average heart-beat-rate tracking accuracy above 97%. On the other hand, this new framework maintains the original signal temporal structure unlike the spectrum-based approach, and it can be further applied for the calculation of blood oxygen level (SpO2).

Neuroimaging of Human Balance Control: A Systematic Review.

This review examined 83 articles using neuroimaging modalities to investigate the neural correlates underlying static and dynamic human balance control, with aims to support future mobile neuroimaging research in the balance control domain. Furthermore, this review analyzed the mobility of the neuroimaging hardware and research paradigms as well as the analytical methodology to identify and remove movement artifact in the acquired brain signal. We found that the majority of static balance control tasks utilized mechanical perturbations to invoke feet-in-place responses (27 out of 38 studies), while cognitive dual-task conditions were commonly used to challenge balance in dynamic balance control tasks (20 out of 32 studies). While frequency analysis and event related potential characteristics supported enhanced brain activation during static balance control, that in dynamic balance control studies was supported by spatial and frequency analysis. Twenty-three of the 50 studies utilizing EEG utilized independent component analysis to remove movement artifacts from the acquired brain signals. Lastly, only eight studies used truly mobile neuroimaging hardware systems. This review provides evidence to support an increase in brain activation in balance control tasks, regardless of mechanical, cognitive, or sensory challenges. Furthermore, the current body of literature demonstrates the use of advanced signal processing methodologies to analyze brain activity during movement. However, the static nature of neuroimaging hardware and conventional balance control paradigms prevent full mobility and limit our knowledge of neural mechanisms underlying balance control.

Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals

In recent years the field of fMRI research has enjoyed expanded technical abilities related to resolution, as well as use across many fields of brain research. At the same time, the field has also dealt with uncertainty related to many known and unknown effects of artifact in fMRI data. In this review we discuss an emerging fMRI technology, called multi-echo (ME)-fMRI, which focuses on improving the fidelity and interpretability of fMRI. Where the essential problem of standard single-echo fMRI is the indeterminacy of sources of signals, whether BOLD or artifact, this is not the case for ME-fMRI. By acquiring multiple echo images per slice, the ME approach allows T2* decay to be modeled at every voxel at every time point. Since BOLD signals arise by changes in T2* over time, an fMRI experiment sampling the T2* signal decay can be analyzed to distinguish BOLD from artifact signal constituents. While the ME approach has a long history of use in theoretical and validation studies, modern MRI systems enable whole-brain multi-echo fMRI at high resolution. This review covers recent multi-echo fMRI acquisition methods, and the analysis steps for this data to make fMRI at once more principled, straightforward, and powerful. After a brief overview of history and theory, T2* modeling and applications will be discussed. These applications include T2* mapping and combining echoes from ME data to increase BOLD contrast and mitigate dropout artifacts. Next, the modeling of fMRI signal changes to detect signal origins in BOLD-related T2* versus artifact-related S0 changes will be reviewed. A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA). After describing how ME-fMRI and ME-ICA lead to a general model for analysis of fMRI signals, applications in animal and human imaging will be discussed. Applications include removing motion artifacts in resting state data at subject and group level. New imaging methods such as multi-band multi-echo fMRI and imaging at 7T are demonstrated throughout the review, and a practical analysis pipeline is described. The review culminates with evidence from recent studies of major boosts in statistical power from using multi-echo fMRI for detecting activation and connectivity in healthy individuals and patients with neuropsychiatric disease. In conclusion, the review shows evidence that the multi-echo approach expands the range of experiments that is practicable using fMRI. These findings suggest a compelling future role of the multi-echo approach in subject-level and clinical fMRI.

Motion correction using overlapped data correlation based on a spatial-spectral encoded parallel optical coherence tomography

This paper presents an approach to remove motion artifacts based on a spatial-spectral encoded parallel OCT (SSE-POCT) system, where encoded rectangular illumination is employed. Motion artifacts within a B-scan are avoided due to parallel detection intrinsic to parallel OCT, while those between successive B-scans are estimated and corrected by a proposed overlapped data correlation (ODC) algorithm. To preserve axial resolution, decoded B-scan corresponding to complete spectrum is stitched from successive encoded B-scans after motion correction. Imaging is conducted on several samples under preset motion trajectories, and OCT images with unnoticed motion artifacts and well-preserved resolutions are reconstructed. The approach based on the developed SSE-POCT system and the proposed ODC algorithm for motion correction can be applicable for in vivo imaging where uncontrolled motion is usually unavoidable.

Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography Without Resorting to Electrooculography.

Magnetoencephelography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from muscle activity often corrupts the data. Eye-blinks are one of the most common types of muscle artifact. They can be recorded by affixing eye proximal electrodes, as in electrooculography (EOG), however this complicates patient preparation and decreases comfort. Moreover, it can induce further muscular artifacts from facial twitching. We propose an EOG free, data driven approach. We begin with Independent Component Analysis (ICA), a well-known preprocessing approach that factors observed signal into statistically independent components. When applied to MEG, ICA can help separate neuronal components from non-neuronal ones, however, the components are randomly ordered. Thus, we develop a method to assign one of two labels, non-eye-blink or eye-blink, to each component. Our contributions are two-fold. First, we develop a 10-layer Convolutional Neural Network (CNN), which directly labels eye-blink artifacts. Second, we visualize the learned spatial features using attention mapping, to reveal what it has learned and bolster confidence in the method's ability to generalize to unseen data. We acquired 8-min, eyes open, resting state MEG from 44 subjects. We trained our method on the spatial maps from ICA of 14 subjects selected randomly with expertly labeled ground truth. We then tested on the remaining 30 subjects. Our approach achieves a test classification accuracy of 99.67%, sensitivity: 97.62%, specificity: 99.77%, and ROC AUC: 98.69%. We also show the learned spatial features correspond to those human experts typically use which corroborates our model's validity. This work (1) facilitates creation of fully automated processing pipelines in MEG that need to remove motion artifacts related to eye blinks, and (2) potentially obviates the use of additional EOG electrodes for the recording of eye-blinks in MEG studies.

Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal

The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR), lambda (λ), root mean square error (RMSE), elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.

Description of a Database Containing Wrist PPG Signals Recorded during Physical Exercise with Both Accelerometer and Gyroscope Measures of Motion

Wearable heart rate sensors such as those found in smartwatches are commonly based upon Photoplethysmography (PPG) which shines a light into the wrist and measures the amount of light reflected back. This method works well for stationary subjects, but in exercise situations, PPG signals are heavily corrupted by motion artifacts. The presence of these artifacts necessitates the creation of signal processing algorithms for removing the motion interference and allowing the true heart related information to be extracted from the PPG trace during exercise. Here, we describe a new publicly available database of PPG signals collected during exercise for the creation and validation of signal processing algorithms extracting heart rate and heart rate variability from PPG signals. PPG signals from the wrist are recorded together with chest electrocardiography (ECG) to allow a reference/comparison heart rate to be found, and the temporal alignment between the two signal sets is estimated from the signal timestamps. The new database differs from previously available public databases because it includes wrist PPG recorded during walking, running, easy bike riding and hard bike riding. It also provides estimates of the wrist movement recorded using a 3-axis low-noise accelerometer, a 3-axis wide-range accelerometer, and a 3-axis gyroscope. The inclusion of gyroscopic information allows, for the first time, separation of acceleration due to gravity and acceleration due to true motion of the sensor. The hypothesis is that the improved motion information provided could assist in the development of algorithms with better PPG motion artifact removal performance.

Automated Detection of Atrial Fibrillation Based on Time–Frequency Analysis of Seismocardiograms

In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119min of AFib data and 126min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of [Formula: see text] and an average true negative rate of [Formula: see text] for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.

Effective EEG Motion Artifact Removal with KS test Blind Source Separation and Wavelet Transform

Effective EEG Motion Artifact Removal with KS test Blind Source Separation and Wavelet Transform

Inspection of the motion artifact removal method from the EEG by using accelerometer

Inspection of the motion artifact removal method from the EEG by using accelerometer

Combining Nonlinear Adaptive Filtering and Signal Decomposition for Motion Artifact Removal in Wearable Photoplethysmography

Heart rate (HR) estimation using photoplethysmography (PPG) has drawn increasing attention in the field of wearable technology due to its advantages with higher degree of usability and lower cost than Electrocardiograph. It has been widely used in wearable devices, such as smart-watches for fitness tracking and vital sign monitoring. However, motion artifact is a strong interference, preventing accurate estimation of HR. Signal decomposition and adaptive filtering are two popular approaches for motion artifact removal, but each of them has inherent drawbacks. In this paper, a hybrid motion artifact removal method is proposed, which combines nonlinear adaptive filtering and signal decomposition, getting the best of both approaches. The method was evaluated on the PPG database used in the 2015 IEEE Signal Processing Cup. The experimental results showed that the method achieved the average absolute error of 1.16 beat per minutes (BPM) on the 12 training data sets, and 2.98 BPM on the ten testing data sets.

Double sensor complementary placement method to reduce motion artifacts in PPG using fast independent component analysis.

A novel sensor placement method for better removal of motion artifacts (MA) from photoplethysmography (PPG) signal using Fast Independent Component Analysis (ICA) is proposed in this paper. The method enhances the determination of pulse transit time (PTT) of PPG signals. The design makes use of double reflectance mode based PPG probes, which are placed complementary to each other and on the two sides of a single finger. Furthermore, a novel indicator denoted as Separating Factor is proposed as well. It helps evaluating the performance of ICA with different sensors configuration. This paper then addresses the effectiveness of FastICA in motion artifacts reduction by using the novel method and normal sensors placement method to capture PPG. Results indicate that better independent source separation can be achieved and morphology of PPG signal is perfectly restored when using the method proposed in this paper.

A wrist worn SpO2 monitor with custom finger probe for motion artifact removal.

Continuous monitoring of blood oxygen saturation (SpO2) level and heart rate is critical in surgery, ICUs and patients suffering from Chronic Obstructive Pulmonary Diseases. Pulse oximeters which compute SpO2 using transmittance photoplethysmography (PPG), is widely accepted for continuous monitoring. Presence of motion artifacts in PPG signals is a major obstacle in the extraction of reliable cardiovascular parameters, in real time and continuous monitoring applications. In this paper, a wrist worn device with a custom finger probe with an integrated accelerometer to remove motion artifacts is presented. An algorithm which can run on low power systems with processing constraints is implemented on the device. The device does continuous acquisition of PPG and accelerometer waveforms and computes SpO2 using the proposed light weight algorithm. The measurement results are continuously synced with an Android tablet, which acts as a gateway and is pushed on to the cloud for further analysis. The accuracy in SpO2 measured by the device was validated using Fluke ProSim 8 SpO2 simulator and the efficiency in accurately computing SpO2 in the presence of motion was validated over 40 healthy volunteers in a controlled setting.

Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis

Abstract In ambulatory electroencephalogram (EEG) health care systems, recorded EEG signals often contaminated by motion artifacts. In this paper, we proposed a singular spectrum analysis (SSA) technique with new grouping criteria to remove the motion artifact from a single channel EEG signal. In order to remove the motion artifact from a single channel EEG signal, we considered the eigenvectors (basis vectors) corresponding to motion artifact are grouped or identified based on their local mobility, which is a signal complexity measure. However, as the local mobility of eigenvectors associated to the motion artifact are small, a threshold of 0.1 is set to identify them. The motion artifact signal is estimated using the identified eigenvectors and subtracted from the contaminated EEG signal to obtain the corrected EEG signal. The proposed technique is tested on 21 single channel real EEG signals contaminated by motion artifact and compared the results with the existing combined ensemble empirical mode decomposition and canonical correlation analysis (EEMD-CCA) technique. The simulation results show that the proposed modified SSA enjoys an improvement in the signal to noise ratio and the percentage reduction in artifact. Moreover, as the ambulatory EEG systems are battery operated, use of high computational signal processing techniques will reduce the battery lifetime. Hence, low computational signal processing techniques are greatly demanded in such applications. Thus, we have also evaluated the computational complexity of the proposed technique and compared with EEMD-CCA. We found that the proposed modified SSA technique significantly reduces the computational complexity and thereby lower power consumption compared to the EEMD-CCA.

Induction and separation of motion artifacts in EEG data using a mobile phantom head device

Objective. Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. Approach. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Main results. Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components’ (ICs) power spectrum (∼15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%–700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Significance. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.