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Refet Firat Yazicioglu - One of the best experts on this subject based on the ideXlab platform.

  • noncontact ecg recording system with real time capacitance measurement for Motion Artifact reduction
    IEEE Transactions on Biomedical Circuits and Systems, 2014
    Co-Authors: Tom Torfs, Yunhsuan Chen, Refet Firat Yazicioglu
    Abstract:

    A system for noncontact ECG recording is proposed that measures the real time electrode-body capacitance concurrently with the ECG as a reference signal for Motion Artifact reduction. Simultaneous recordings of these two signals from the human body in the presence of electrode Motion Artifacts are shown and an adaptive least-mean-squares (LMS) filtering algorithm run on these signals is demonstrated to be able to reduce the severity of certain types of electrode Motion Artifacts.

  • a 160 mu rm a biopotential acquisition ic with fully integrated ia and Motion Artifact suppression
    IEEE Transactions on Biomedical Circuits and Systems, 2012
    Co-Authors: N Van Helleputte, Hyejung Kim, Sunyoung Kim, Jong Pal Kim, C Van Hoof, Refet Firat Yazicioglu
    Abstract:

    This paper proposes a 3-channel biopotential monitoring ASIC with simultaneous electrode-tissue impedance measurements which allows real-time estimation of Motion Artifacts on each channel using an an external μC. The ASIC features a high performance instrumentation amplifier with fully integrated sub-Hz HPF rejecting rail-to-rail electrode-offset voltages. Each readout channel further has a programmable gain amplifier and programmable 4th order low-pass filter. Time-multiplexed 12 b SAR-ADCs are used to convert all the analog data to digital. The ASIC achieves >; 115 dB of CMRR (at 50/60 Hz), a high input impedance of >; 1 GΩ and low noise (1.3 μVrms in 100 Hz). Unlike traditional methods, the ASIC is capable of actual Motion Artifact suppression in the analog domain before final amplification. The complete ASIC core operates from 1.2 V with 2 V digital IOs and consumes 200 μW when all 3 channels are active.

  • correlation between electrode tissue impedance and Motion Artifact in biopotential recordings
    IEEE Sensors Journal, 2012
    Co-Authors: Dilpreet Buxi, Julien Penders, Refet Firat Yazicioglu, Sunyoung Kim, N Van Helleputte, Marco Altini, Jacqueline Wijsman, C Van Hoof
    Abstract:

    Ambulatory monitoring of the electrocardiogram (ECG) is a highly relevant topic in personal healthcare. A key technical challenge is overcoming Artifacts from Motion in order to produce ECG signals capable of being used in clinical diagnosis by a cardiologist. An electrode-tissue impedance is a signal of significant interest in reducing the Motion Artifact in ECG recordings on the go. A wireless system containing an ultralow-power analog front-end ECG signal acquisition, as well as the electrode-tissue impedance, is used in a validation study on multiple subjects. The goal of this paper is to study the correlation between Motion Artifacts and skin electrode impedance for a variety of Motion types and electrodes. We have found that the correlation of the electrode-tissue impedance with the Motion Artifact is highly dependent on the electrode design the impedance signal (real, imaginary, absolute impedance), and Artifact types (e.g., push or pull electrodes). With the chosen electrodes, we found that the highest correlation was obtained for local electrode Artifacts (push, pull, electrode) followed by local skin (stretch, twist, skin) and global Artifacts (walk, jog, jump). The results show that the electrode-tissue impedance can correlate with the Motion Artifacts for local disturbance of the electrodes and that the impedance signals can be used in Motion Artifact removal techniques such as adaptive filtering.

  • real time digitally assisted analog Motion Artifact reduction in ambulatory ecg monitoring system
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Nick Van Helleputte, Chris Van Hoof, Refet Firat Yazicioglu
    Abstract:

    This paper proposes a real time digitally assisted analog Motion Artifact reduction ASIC with ECG measurement simultaneously. It features one ECG monitoring and in- and quad-phase electrode-skin impedance measurement, which are used to estimate Motion Artifacts. The implemented ASIC is capable of actual Motion Artifact reduction in the analog domain before final amplification.

  • Motion Artifact removal using cascade adaptive filtering for ambulatory ecg monitoring system
    Biomedical Circuits and Systems Conference, 2012
    Co-Authors: Nick Van Helleputte, Inaki Romero, Torfinn Berset, Julien Penders, Chris Van Hoof, Di Geng, Refet Firat Yazicioglu
    Abstract:

    A Motion Artifact removal method with a two-stage cascade LMS adaptive filter is proposed for an ambulatory ECG monitoring system. The first LMS stage consisting of analog feedback prevents the signal saturation to reduce the input dynamic range. An adaptive step-size LMS algorithm is introduced and employed for the second LMS stage. The adaptive step-size algorithm can achieve fast convergence to track large sudden Motion Artifact quickly, while preventing the distortion of the ECG component. The filtering performance is evaluated by the heart beat detection, measured by sensitivity (Se) and positive predictive value (+p), and the performance is increased by 9.8% and 6.48%, respectively, compared to the unfiltered signal at the worst case with -25dB SNR. The proposed Motion Artifact method is implemented on an ambulatory ECG monitoring module, and the real-time measurement shows a significant performance improvement.

Takashi Kawabata - One of the best experts on this subject based on the ideXlab platform.

  • Motion Artifact cancellation and outlier rejection for clip type ppg based heart rate sensor
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2015
    Co-Authors: Takunori Shimazaki, Shinsuke Hara, Hiroyuki Okuhata, Hajime Nakamura, Takashi Kawabata
    Abstract:

    Heart rate sensing can be used to not only understand exercise intensity but also detect life-critical condition during sports activities. To reduce stress during exercise and attach heart rate sensor easily, we developed a clip-type photoplethysmography (PPG)-based heart rate sensor. The sensor can be attached just by hanging it to the waist part of undershorts, and furthermore, it employs the Motion Artifact (MA) cancellation technique. However, due to its low contact pressure, sudden jumps and drops, which are called “outliers,” are often observed in the sensed heart rate, so we also developed a simple outlier rejection technique. By an experiment using five male subjects (4 sets per subject), we confirmed the MA cancellation and outlier rejection capabilities.

  • Cancellation of Motion Artifact induced by exercise for PPG-based heart rate sensing
    2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
    Co-Authors: Takunori Shimazaki, Shinsuke Hara, Hiroyuki Okuhata, Hajime Nakamura, Takashi Kawabata
    Abstract:

    Heart rate (HR) sensing during exercise is essential for medical, healthcare and sport physiological purposes. Photo-Plethysmo-Graphy (PPG) is a simple and non-invasive technique for HR sensing, but it is highly sensitive to Motion Artifact. This paper proposes a cancellation technique of Motion Artifact in PPG-based HR sensing for a man during exercise. The canceller is equipped with two sensors; one is a normal PPG sensor where an LED/Photo-Detector (PD) contacts the skin to detect Blood Volume Pulse (BVP) (+Motion Artifact) and the other is a Motion Artifact sensor where an LED/PD does not contact the skin to detect only Motion Artifact. Experimental results show that the proposed technique, which is implemented in adaptive algorithms, can sense HR correctly by cancelling Motion Artifact induced by exercises such as running and jumping.

Phillip M Boiselle - One of the best experts on this subject based on the ideXlab platform.

  • Motion Artifact on high resolution ct images of pediatric patients comparison of volumetric and axial ct methods
    American Journal of Roentgenology, 2009
    Co-Authors: Maria Dalmeida Bastos, Edward Y Lee, Keith J Strauss, David Zurakowski, Donald A Tracy, Phillip M Boiselle
    Abstract:

    OBJECTIVE. The purpose of this study was to address the controversy whether the quality of volumetric high-resolution CT (HRCT) images is as good as that of axial nonvolumetric HRCT images by assessing the degree of Motion Artifact on images acquired with the two methods at MDCT of pediatric patients with known or suspected lung disease.MATERIALS AND METHODS. A search of the hospital information system was conducted to identify the cases of pediatric patients with clinically suspected or known interstitial lung disease who underwent 16-MDCT of the chest with both volumetric and axial HRCT acquisitions (both 1.25-mm slice thickness) from March 2005 to July 2008. Two pediatric radiologists reviewed the images for the presence of Motion Artifacts at three anatomic levels (upper, middle, and lower lung zones). Motion Artifacts were given numerical grades representing no Artifact to severe Artifact, and the paired Student's t test was used to compare the scores for the two acquisition methods. A total Motion s...

  • high resolution ct using mdct comparison of degree of Motion Artifact between volumetric and axial methods
    American Journal of Roentgenology, 2004
    Co-Authors: D M Kelly, Ichiro Hasegawa, R Borders, Hiroto Hatabu, Phillip M Boiselle
    Abstract:

    OBJECTIVE. The purpose of this study was to compare the degree of Motion Artifact on high-resolution CT images obtained using volumetric and axial (nonvolumetric) CT methods.CONCLUSION. Volumetric high-resolution CT is associated with significantly greater Motion Artifact compared with axial noncontiguous high-resolution imaging.

Danielle S Bassett - One of the best experts on this subject based on the ideXlab platform.

  • evaluating the sensitivity of functional connectivity measures to Motion Artifact in resting state fmri data
    NeuroImage, 2021
    Co-Authors: Arun S Mahadevan, Allyson P. Mackey, Ursula A Tooley, Maxwell A Bertolero, Danielle S Bassett
    Abstract:

    Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head Motion Artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to Motion Artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with Motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for Motion Artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to Motion Artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by Motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with Motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

  • evaluating the sensitivity of functional connectivity measures to Motion Artifact in resting state fmri data
    bioRxiv, 2020
    Co-Authors: Arun S Mahadevan, Allyson P. Mackey, Ursula A Tooley, Maxwell A Bertolero, Danielle S Bassett
    Abstract:

    Abstract Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head Motion Artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to Motion Artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with Motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for Motion Artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to Motion Artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by Motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with Motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

  • Mitigating head Motion Artifact in functional connectivity MRI
    Nature Protocols, 2018
    Co-Authors: Rastko Ciric, Danielle S Bassett, Adon F. G. Rosen, Guray Erus, Matthew Cieslak, Azeez Adebimpe, Philip A. Cook, Christos Davatzikos, Daniel H. Wolf, Theodore D. Satterthwaite
    Abstract:

    Ciric et al. describe a protocol for the removal of Motion Artifacts from functional MRI data. They introduce a software package that implements common denoising protocols and provides tools for assessing the efficacy of denoising. Participant Motion during functional magnetic resonance image (fMRI) acquisition produces spurious signal fluctuations that can confound measures of functional connectivity. Without mitigation, Motion Artifact can bias statistical inferences about relationships between connectivity and individual differences. To counteract Motion Artifact, this protocol describes the implementation of a validated, high-performance denoising strategy that combines a set of model features, including physiological signals, Motion estimates, and mathematical expansions, to target both widespread and focal effects of subject movement. This protocol can be used to reduce Motion-related variance to near zero in studies of functional connectivity, providing up to a 100-fold improvement over minimal-processing approaches in large datasets. Image denoising requires 40 min to 4 h of computing per image, depending on model specifications and data dimensionality. The protocol additionally includes instructions for assessing the performance of a denoising strategy. Associated software implements all denoising and diagnostic procedures, using a combination of established image-processing libraries and the eXtensible Connectivity Pipeline (XCP) software.

Takunori Shimazaki - One of the best experts on this subject based on the ideXlab platform.

  • Motion Artifact cancellation and outlier rejection for clip type ppg based heart rate sensor
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2015
    Co-Authors: Takunori Shimazaki, Shinsuke Hara, Hiroyuki Okuhata, Hajime Nakamura, Takashi Kawabata
    Abstract:

    Heart rate sensing can be used to not only understand exercise intensity but also detect life-critical condition during sports activities. To reduce stress during exercise and attach heart rate sensor easily, we developed a clip-type photoplethysmography (PPG)-based heart rate sensor. The sensor can be attached just by hanging it to the waist part of undershorts, and furthermore, it employs the Motion Artifact (MA) cancellation technique. However, due to its low contact pressure, sudden jumps and drops, which are called “outliers,” are often observed in the sensed heart rate, so we also developed a simple outlier rejection technique. By an experiment using five male subjects (4 sets per subject), we confirmed the MA cancellation and outlier rejection capabilities.

  • breathing Motion Artifact cancellation in ppg based heart rate sensing
    International Symposium on Medical Information and Communication Technology, 2015
    Co-Authors: Takunori Shimazaki, Shinsuke Hara
    Abstract:

    We have developed a photoplethysmography (PPG)-based heart rate (HR) sensor [1], which is equipped with two sensors; one is a normal PPG sensor and the other is a Motion Artifact sensor. In the PPG-based HR sensor, applying an adaptive filter algorithm for the outputs from the two sensors, it can effectively cancel Motion Artifact induced by vigorous exercise. Breathing Motion is also one of the main sources of Artifact in PPG-based HR sensing. In this paper, we evaluate the cancellation performance of breathing Motion Artifact by the PPG-based HR sensor in experiments using five subjects.

  • Cancellation of Motion Artifact induced by exercise for PPG-based heart rate sensing
    2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
    Co-Authors: Takunori Shimazaki, Shinsuke Hara, Hiroyuki Okuhata, Hajime Nakamura, Takashi Kawabata
    Abstract:

    Heart rate (HR) sensing during exercise is essential for medical, healthcare and sport physiological purposes. Photo-Plethysmo-Graphy (PPG) is a simple and non-invasive technique for HR sensing, but it is highly sensitive to Motion Artifact. This paper proposes a cancellation technique of Motion Artifact in PPG-based HR sensing for a man during exercise. The canceller is equipped with two sensors; one is a normal PPG sensor where an LED/Photo-Detector (PD) contacts the skin to detect Blood Volume Pulse (BVP) (+Motion Artifact) and the other is a Motion Artifact sensor where an LED/PD does not contact the skin to detect only Motion Artifact. Experimental results show that the proposed technique, which is implemented in adaptive algorithms, can sense HR correctly by cancelling Motion Artifact induced by exercises such as running and jumping.