21 research outputs found
Colour morphological sieves for scale-space image processing
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Advancements and Breakthroughs in Ultrasound Imaging
Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world
Micro-, Meso- and Macro-Dynamics of the Brain
Neurosciences, Neurology, Psychiatr
Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods
Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram
(ECG) represent the complex dynamic behaviours of biological systems.
The analysis of these signals using variety of nonlinear methods is essential
for understanding variability within EEG and ECG, which potentially
could help unveiling hidden patterns related to underlying physiological mechanisms.
EEG is a time varying signal, and electrodes for recording EEG at different
positions on the scalp give different time varying signals. There might
be correlation between these signals. It is important to know the correlation
between EEG signals because it might tell whether or not brain activities from
different areas are related. EEG and ECG might be related to each other because
both of them are generated from one co-ordinately working body. Investigating
this relationship is of interest because it may reveal information about
the correlation between EEG and ECG signals.
This thesis is about assessing variability of time series data, EEG and ECG, using
variety of nonlinear measures. Although other research has looked into the
correlation between EEGs using a limited number of electrodes and a limited
number of combinations of electrode pairs, no research has investigated the
correlation between EEG signals and distance between electrodes. Furthermore,
no one has compared the correlation performance for participants with
and without medical conditions. In my research, I have filled up these gaps
by using a full range of electrodes and all possible combinations of electrode
pairs analysed in Time Domain (TD). Cross-Correlation method is calculated
on the processed EEG signals for different number unique electrode pairs from
each datasets. In order to obtain the distance in centimetres (cm) between
electrodes, a measuring tape was used. For most of our participants the head
circumference range was 54-58cm, for which a medium-sized I have discovered
that the correlation between EEG signals measured through electrodes
is linearly dependent on the physical distance (straight-line) distance between
them for datasets without medical condition, but not for datasets with medical
conditions.
Some research has investigated correlation between EEG and Heart Rate Variability
(HRV) within limited brain areas and demonstrated the existence of
correlation between EEG and HRV. But no research has indicated whether or
not the correlation changes with brain area. Although Wavelet Transformations
(WT) have been performed on time series data including EEG and HRV
signals to extract certain features respectively by other research, so far correlation
between WT signals of EEG and HRV has not been analysed. My research
covers these gaps by conducting a thorough investigation of all electrodes on
the human scalp in Frequency Domain (FD) as well as TD. For the reason of
different sample rates of EEG and HRV, two different approaches (named as
Method 1 and Method 2) are utilised to segment EEG signals and to calculate
Pearson’s Correlation Coefficient for each of the EEG frequencies with each
of the HRV frequencies in FD. I have demonstrated that EEG at the front area
of the brain has a stronger correlation with HRV than that at the other area in
a frequency domain. These findings are independent of both participants and
brain hemispheres.
Sample Entropy (SE) is used to predict complexity of time series data. Recent
research has proposed new calculation methods for SE, aiming to improve the
accuracy. To my knowledge, no one has attempted to reduce the computational
time of SE calculation. I have developed a new calculation method for time
series complexity which could improve computational time significantly in the
context of calculating a correlation between EEG and HRV. The results have
a parsimonious outcome of SE calculation by exploiting a new method of SE
implementation. In addition, it is found that the electrical activity in the frontal
lobe of the brain appears to be correlated with the HRV in a time domain.
Time series analysis method has been utilised to study complex systems that
appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing
variables affecting stock values). In this thesis, I have also investigated the nature
of the dynamic system of HRV. I have disclosed that Embedding Dimension
could unveil two variables that determined HRV
Scientific Advances in STEM: From Professor to Students
This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields
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Neuronal dynamics and connectivity analysis of neuronal cultures on multi electrode arrays
Despite a number of attempts over the past two decades, research into reliable, controlled induction of long term evoked responses, mimicking low level learning and memory in dissociated cell cultures remains challenging. In addition, a full understanding of the stimulus-response relationships that underlie synaptic plasticity has not yet been achieved, and many of the underlying principles remain largely unknown. Plasticity studies have been predominantly limited to low density Multi/Micro Electrode Arrays (MEAs). With the advent of complementary metal-oxide-semiconductor (CMOS) based High-Density (HD) MEAs, unprecedented spatial and temporal resolution is now possible. In this thesis, an attempt to bridge the gap between studies of neural plasticity and the use of CMOS based HD-MEAs with thousands of electrodes, is reported. Additionally, since such HD-MEAs generate a large volume of data and require advanced analytics to efficiently process and analyse recordings, computational tools and novel algorithms to infer connectivity during plasticity have been developed.
The study showed that the responsiveness, stability and initial firing rate of neuronal cultures are the deciding factors to reliably induce evoked responses. With multi-site stimulation, sustained long term potentiation was achieved, which was validated both by evoked response plots and overall firing rates measured at five different time points - before and after repeated stimulation, and at a three day time points. In contrast, while depression responses were observed, it was found that the effects were not sustained over many days. The findings of the study suggest that appropriate selection of neuronal cultures is crucial for inducing desired evoked responses and criteria for this have been developed. Furthermore, it is concluded that the initial responses to test stimuli can be used to determine whether potentiated or depressed responses are to be expected.
To analyse the recordings, pipeline of computational tools was developed. Firstly, neuronal synchrony metrics were adapted for the first time for large HD-MEA recordings and shown to correspond effectively to the firing dynamics. To analyse functional connectivity, an information theoretic approach, Transfer Entropy(TE), was utilised. The method showed accurate estimation of functional connectivity with mid 80th percentile accuracy on simulated data. A superimposition method was proposed to enhance confidence in the connectivity estimation. To statistically evaluate connectivity estimation, a new surrogate method, based on ISI distribution approach, was proposed and validated with a simulated Izhikevich network. The method achieved improved accuracy, compared to the existing ISI shuffling method. This newly developed method was later utilised to infer connectivity and refine connections during the learning process of real neuronal cultures over many days of stimulation. The connectivity inference corresponded accurately to both the spontaneous and stimulated networks during evoked responses and the proposed method permitted observation of the evolution of connections for the potentiated network