4,766 research outputs found
Motion Artifact Processing Techniques for Physiological Signals
The combination of reducing birth rate and increasing life expectancy continues to drive
the demographic shift toward an ageing population and this is placing an ever-increasing
burden on our healthcare systems. The urgent need to address this so called healthcare
\time bomb" has led to a rapid growth in research into ubiquitous, pervasive and
distributed healthcare technologies where recent advances in signal acquisition, data
storage and communication are helping such systems become a reality. However, similar
to recordings performed in the hospital environment, artifacts continue to be a major
issue for these systems. The magnitude and frequency of artifacts can vary signicantly
depending on the recording environment with one of the major contributions due to
the motion of the subject or the recording transducer. As such, this thesis addresses
the challenges of the removal of this motion artifact removal from various physiological
signals.
The preliminary investigations focus on artifact identication and the tagging of physiological
signals streams with measures of signal quality. A new method for quantifying
signal quality is developed based on the use of inexpensive accelerometers which facilitates
the appropriate use of artifact processing methods as needed. These artifact
processing methods are thoroughly examined as part of a comprehensive review of the
most commonly applicable methods. This review forms the basis for the comparative
studies subsequently presented. Then, a simple but novel experimental methodology
for the comparison of artifact processing techniques is proposed, designed and tested
for algorithm evaluation. The method is demonstrated to be highly eective for the
type of artifact challenges common in a connected health setting, particularly those concerned
with brain activity monitoring. This research primarily focuses on applying the
techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography
(EEG) data due to their high susceptibility to contamination by subject motion related
artifact.
Using the novel experimental methodology, complemented with simulated data, a comprehensive
comparison of a range of artifact processing methods is conducted, allowing
the identication of the set of the best performing methods. A novel artifact removal
technique is also developed, namely ensemble empirical mode decomposition with canonical
correlation analysis (EEMD-CCA), which provides the best results when applied on
fNIRS data under particular conditions. Four of the best performing techniques were
then tested on real ambulatory EEG data contaminated with movement artifacts comparable
to those observed during in-home monitoring.
It was determined that when analysing EEG data, the Wiener lter is consistently
the best performing artifact removal technique. However, when employing the fNIRS
data, the best technique depends on a number of factors including: 1) the availability
of a reference signal and 2) whether or not the form of the artifact is known. It is
envisaged that the use of physiological signal monitoring for patient healthcare will grow
signicantly over the next number of decades and it is hoped that this thesis will aid in
the progression and development of artifact removal techniques capable of supporting
this growth
Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects.
The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively
Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression
Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies
and novel treatments of major depressive disorder (MDD). EEG performed
simultaneously with an rtfMRI-nf procedure allows an independent evaluation of
rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is
a widely used measure of emotion and motivation that shows profound changes in
depression. However, it has never been directly related to simultaneously
acquired fMRI data. We report the first study investigating
electrophysiological correlates of the rtfMRI-nf procedure, by combining
rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study,
MDD patients in the experimental group (n=13) learned to upregulate BOLD
activity of the left amygdala using an rtfMRI-nf during a happy emotion
induction task. MDD patients in the control group (n=11) were provided with a
sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha
band and BOLD activity across the brain were examined. Average individual
changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental
group showed a significant positive correlation with the MDD patients'
depression severity ratings, consistent with an inverse correlation between the
depression severity and frontal EEG asymmetry at rest. Temporal correlations
between frontal EEG asymmetry and BOLD activity were significantly enhanced,
during the rtfMRI-nf task, for the amygdala and many regions associated with
emotion regulation. Our findings demonstrate an important link between amygdala
BOLD activity and frontal EEG asymmetry. Our EEG asymmetry results suggest that
the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients,
and that alpha-asymmetry EEG-nf would be compatible with the amygdala
rtfMRI-nf. Combination of the two could enhance emotion regulation training and
benefit MDD patients.Comment: 28 pages, 16 figures, to appear in NeuroImage: Clinica
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis
The electroencephalogram (EEG) and functional near-infrared spectroscopy
(fNIRS) signals, highly non-stationary in nature, greatly suffers from motion
artifacts while recorded using wearable sensors. This paper proposes two robust
methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with
canonical correlation analysis (WPD-CCA), for motion artifact correction from
single-channel EEG and fNIRS signals. The efficacy of these proposed techniques
is tested using a benchmark dataset and the performance of the proposed methods
is measured using two well-established performance matrices: i) Difference in
the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion
artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts
correction technique produces the highest average {\Delta}SNR (29.44 dB) when
db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%)
is obtained using db1 wavelet packet for all the available 23 EEG recordings.
Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA
method utilizing db1 wavelet packet has shown the best denoising performance
producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%,
respectively for all the EEG recordings. On the other hand, the two-stage
motion artifacts removal technique i.e. WPD-CCA has produced the best average
{\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta}
(41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta}
using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and
26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In
both EEG and fNIRS modalities, the percentage reduction in motion artifacts
increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques
are employed.Comment: 25 pages, 10 figures and 2 table
Emotions Detection based on a Single-electrode EEG Device
The study of emotions using multiple channels of EEG represents a widespread practice in the field of research
related to brain computer interfaces (Brain Computer Interfaces). To date, few studies have been reported in
the literature with a reduced number of channels, which when used in the detection of emotions present results
that are less accurate than the rest. To detect emotions using an EEG channel and the data obtained is useful
for classifying emotions with an accuracy comparable to studies in which there is a high number of channels,
is of particular interest in this research framework. This article uses the Neurosky Maindwave device; which
has a single electrode to acquire the EEG signal, Matlab software and IBM SPSS Modeler; which process
and classify the signals respectively. The accuracy obtained in the detection of emotions in relation to the
economic resources of the hardware dedicated to the acquisition of EEG signal is remarkable
Artifact Removal Methods in EEG Recordings: A Review
To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods
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