29 research outputs found
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
Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)
1st Doctoral Consortium at the European Conference on
Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020
Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option
Machine Learning Methods with Noisy, Incomplete or Small Datasets
In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios
Smart Sensors for Healthcare and Medical Applications
This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare
Analysis of Embodied and Situated Systems from an Antireductionist Perspective
The analysis of embodied and situated agents form a dynamical system perspective is often
limited to a geometrical and qualitative description. However, a quantitative analysis is necessary
to achieve a deep understanding of cognitive facts.
The field of embodied cognition is multifaceted, and the first part of this thesis is devoted to exploring
the diverse meanings proposed in the existing literature. This is a preliminary fundamental
step as the creation of synthetic models requires well-founded theoretical and foundational
boundaries for operationalising the concept of embodied and situated cognition in a concrete
neuro-robotic model. By accepting the dynamical system view the agent is conceived as highly
integrated and strictly coupled with the surrounding environment. Therefore the antireductionist
framework is followed during the analysis of such systems, using chaos theory to unveil global
properties and information theory to describe the complex network of interactions among the
heterogeneous sub-components.
In the experimental section, several evolutionary robotics experiments are discussed. This class
of adaptive systems is consistent with the proposed definition of embodied and situated cognition.
In fact, such neuro-robotics platforms autonomously develop a solution to a problem exploiting
the continuous sensorimotor interaction with the environment.
The first experiment is a stress test for chaos theory, a mathematical framework that studies erratic
behaviour in low-dimensional and deterministic dynamical systems. The recorded dataset
consists of the robots’ position in the environment during the execution of the task. Subsequently,
the time series is projected onto a multidimensional phase space in order to study the underlying
dynamic using chaotic numerical descriptors. Finally, such measures are correlated and confronted
with the robots’ behavioural strategy and the performance in novel and unpredictable
environments.
The second experiment explores the possible applications of information-theoretic measures for
the analysis of embodied and situated systems. Data is recorded from perceptual and motor
neurons while robots are executing a wall-following task and pairwise estimations of the mutual
information and the transfer entropy are calculated in order to create an exhaustive map of the
nonlinear interactions among variables. Results show that the set of information-theoretic employed
in this study unveils characteristics of the agent-environemnt interaction and the functional
neural structure.
This work aims at testing the explanatory power and impotence of nonlinear time series analysis
applied to observables recorded from neuro-robotics embodied and situated systems
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio