Abstract: Moving into a new era of healthcare, new tools and
devices are developed to extend and improve health services, such as
remote patient monitoring and risk prevention. In this concept,
Internet of Things (IoT) and Cloud Computing present great
advantages by providing remote and efficient services, as well as
cooperation between patients, clinicians, researchers and other health
professionals. This paper focuses on patients suffering from bipolar
disorder, a brain disorder that belongs to a group of conditions
called affective disorders, which is characterized by great mood
swings. We exploit the advantages of Semantic Web and Cloud
Technologies to develop a patient monitoring system to support
clinicians. Based on intelligently filtering of evidence-knowledge and
individual-specific information we aim to provide treatment
notifications and recommended function tests at appropriate times or
concluding into alerts for serious mood changes and patient’s nonresponse
to treatment. We propose an architecture as the back-end
part of a cloud platform for IoT, intertwining intelligence devices
with patients’ daily routine and clinicians’ support.
Abstract: Neuroplasticity or the flexibility of the neural system
is the ability of the brain to adapt to the lack or deterioration of sense
and the capability of the neural system to modify itself through
changing shape and function. Not only have studies revealed that
neuroplasticity does not end in childhood, but also they have proven
that it continues till the end of life and is not limited to the neural
system and covers the cognitive system as well. In the field of
cognition, neuroplasticity is defined as the ability to change old
thoughts according to new conditions and the individuals' differences
in using various styles of cognitive regulation inducing several social,
emotional and cognitive outcomes. This paper attempts to discuss and
define major theories and principles of neuroplasticity and elaborate
on nature or nurture.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
Abstract: Content Based Image Retrieval (CBIR) coupled with
Case Based Reasoning (CBR) is a paradigm that is becoming
increasingly popular in the diagnosis and therapy planning of medical
ailments utilizing the digital content of medical images. This paper
presents a survey of some of the promising approaches used in the
detection of abnormalities in retina images as well in
mammographic screening and detection of regions of interest
in MRI scans of the brain. We also describe our proposed
algorithm to detect hard exudates in fundus images of the
retina of Diabetic Retinopathy patients.
Abstract: Diminished antioxidant defense or increased
production of reactive oxygen species in the biological system can
result in oxidative stress which may lead to various
neurodegenerative diseases including Alzheimer’s disease (AD).
Microglial activation also contributes to the progression of AD by
producing several proinflammatory cytokines, nitric oxide (NO) and
prostaglandin E2 (PGE2). Oxidative stress and inflammation have
been reported to be possible pathophysiological mechanisms
underlying AD. In addition, the cholinergic hypothesis postulates that
memory impairment in patient with AD is also associated with the
deficit of cholinergic function in the brain. Although a number of
drugs have been approved for the treatment of AD, most of these
synthetic drugs have diverse side effects and yield relatively modest
benefits. Marine algae have great potential in pharmaceutical and
biomedical applications as they are valuable sources of bioactive
properties such as anticoagulation, antimicrobial, antioxidative,
anticancer and anti-inflammatory. Hence, this study aimed to provide
an overview of the properties of Malaysian seaweeds (Padina
australis, Sargassum polycystum and Caulerpa racemosa) in
inhibiting oxidative stress, neuroinflammation and cholinesterase
enzymes. These seaweeds significantly exhibited potent DPPH and
moderate superoxide anion radical scavenging ability (P
Abstract: Near-infrared spectroscopy (NIRS) has been widely
used as a non-invasive method to measure brain activity, but it is
corrupted by baseline drift noise. Here we present a method to measure
regional cerebral blood flow as a derivative of NIRS output. We
investigate whether, when listening to languages, blood flow can
reasonably localize and represent regional brain activity or not. The
prefrontal blood flow distribution pattern when advanced
second-language listeners listened to a second language (L2) was most
similar to that when listening to their first language (L1) among the
patterns of mean and standard deviation. In experiments with 25
healthy subjects, the maximum blood flow was localized to the left
BA46 of advanced listeners. The blood flow presented is robust to
baseline drift and stably localizes regional brain activity.
Abstract: The study of the electrical signals produced by neural
activities of human brain is called Electroencephalography. In this
paper, we propose an automatic and efficient EEG signal
classification approach. The proposed approach is used to classify the
EEG signal into two classes: epileptic seizure or not. In the proposed
approach, we start with extracting the features by applying Discrete
Wavelet Transform (DWT) in order to decompose the EEG signals
into sub-bands. These features, extracted from details and
approximation coefficients of DWT sub-bands, are used as input to
Principal Component Analysis (PCA). The classification is based on
reducing the feature dimension using PCA and deriving the supportvectors
using Support Vector Machine (SVM). The experimental are
performed on real and standard dataset. A very high level of
classification accuracy is obtained in the result of classification.
Abstract: In this paper, Bayesian online inference in models of
data series are constructed by change-points algorithm, which
separated the observed time series into independent series and study
the change and variation of the regime of the data with related
statistical characteristics. variation of statistical characteristics of time
series data often represent separated phenomena in the some
dynamical system, like a change in state of brain dynamical reflected
in EEG signal data measurement or a change in important regime of
data in many dynamical system. In this paper, prediction algorithm
for studying change point location in some time series data is
simulated. It is verified that pattern of proposed distribution of data
has important factor on simpler and smother fluctuation of hazard
rate parameter and also for better identification of change point
locations. Finally, the conditions of how the time series distribution
effect on factors in this approach are explained and validated with
different time series databases for some dynamical system.
Abstract: Periventricular Leukomalacia (PVL) is a White Matter
Injury (WMI) of preterm neonatal brain. Objectives of the study were
to assess the neuro-developmental outcome at one year of age and to
determine a good protocol of cranial ultrasonography to detect PVL.
Two hundred and sixty four preterm neonates were included in the
study. Series of cranial ultrasound scans were done by using a
dedicated neonatal head probe 4-10 MHz of Logic e portable
ultrasound scanner. Clinical history of seizures, abnormal head
growth (hydrocephalus or microcephaly) and developmental
milestones were assessed and neurological examinations were done
until one year of age. Among live neonates, 57% who had cystic PVL
(Grades 2 and 3) manifested as cerebral palsy. In conclusion cystic
PVL has permanent neurological disabilities like cerebral palsy.
Good protocol of real time cranial ultrasonography to detect PVL is
to perform scans at least once a week until one month and at term (40
weeks of gestation).
Abstract: With a long history, dual-task has become one of the
most intriguing research fields regarding human brain functioning
and cognition. However, findings considering effects of taskinterrelations
are limited (especially, in combined motor and
cognitive tasks). Therefore, we aimed at developing a measurement
system in order to analyse interrelation effects of cognitive and motor
tasks. On the one hand, the present study demonstrates the
applicability of the measurement system and on the other hand first
results regarding a systematisation of different task combinations are
shown. Future investigations should combine imagine technologies
and this developed measurement system.
Abstract: Medulloblastoma (MB) is one of the most prevalent
brain tumours among paediatrics. Although its management has
evolved over time still there is need to find new therapeutic targets
for MB that can result in less normal tissue toxicity while improving
survival and reducing recurrence. This literature review is aimed at
finding new potential therapeutic targets for MB focusing on viruses
that can be used as potential targets for MB. The review also gives an
over-view of management of paediatric Medulloblastoma focusing on
Radiotherapy management.
Abstract: Many of the ever-growing elderly population require
exercise, such as running, for health management. One important
element of a runner’s training is the choice of shoes for exercise; shoes
are important because they provide the interface between the feet and
road. When we purchase shoes, we may instinctively choose a pair
after trying on many different pairs of shoes. Selecting the shoes
instinctively may work, but it does not guarantee a suitable fit for
running activities. Therefore, if we could select suitable shoes for each
runner from the viewpoint of brain activities, it would be helpful for
validating shoe selection. In this paper, we describe how brain
activities show different characteristics during particular task,
corresponding to different properties of shoes. Using five subjects, we
performed a verification experiment, applying weight, softness, and
flexibility as shoe properties. In order to affect the shoe property’s
differences to the brain, subjects run for 10 min. Before and after
running, subjects conducted a paced auditory serial addition task
(PASAT) as the particular task; and the subjects’ brain activities
during the PASAT are evaluated based on oxyhemoglobin and
deoxyhemoglobin relative concentration changes, measured by
near-infrared spectroscopy (NIRS). When the brain works actively,
oxihemoglobin and deoxyhemoglobin concentration drastically
changes; therefore, we calculate the maximum values of concentration
changes. In order to normalize relative concentration changes after
running, the maximum value are divided by before running maximum
value as evaluation parameters. The classification of the groups of
shoes is expressed on a self-organizing map (SOM). As a result,
deoxyhemoglobin can make clusters for two of the three types of
shoes.
Abstract: Tumor is an uncontrolled growth of tissues in any part
of the body. Tumors are of different types and they have different
characteristics and treatments. Brain tumor is inherently serious and
life-threatening because of its character in the limited space of the
intracranial cavity (space formed inside the skull). Locating the tumor
within MR (magnetic resonance) image of brain is integral part of the
treatment of brain tumor. This segmentation task requires
classification of each voxel as either tumor or non-tumor, based on
the description of the voxel under consideration. Many studies are
going on in the medical field using Markov Random Fields (MRF) in
segmentation of MR images. Even though the segmentation process
is better, computing the probability and estimation of parameters is
difficult. In order to overcome the aforementioned issues, Conditional
Random Field (CRF) is used in this paper for segmentation, along
with the modified artificial bee colony optimization and modified
fuzzy possibility c-means (MFPCM) algorithm. This work is mainly
focused to reduce the computational complexities, which are found in
existing methods and aimed at getting higher accuracy. The
efficiency of this work is evaluated using the parameters such as
region non-uniformity, correlation and computation time. The
experimental results are compared with the existing methods such as
MRF with improved Genetic Algorithm (GA) and MRF-Artificial
Bee Colony (MRF-ABC) algorithm.
Abstract: The handwriting is a physical demonstration of a
complex cognitive process learnt by man since his childhood. People
with disabilities or suffering from various neurological diseases are
facing so many difficulties resulting from problems located at the
muscle stimuli (EMG) or signals from the brain (EEG) and which
arise at the stage of writing. The handwriting velocity of the same
writer or different writers varies according to different criteria: age,
attitude, mood, writing surface, etc. Therefore, it is interesting to
reconstruct an experimental basis records taking, as primary
reference, the writing speed for different writers which would allow
studying the global system during handwriting process. This paper
deals with a new approach of the handwriting system modeling based
on the velocity criterion through the concepts of artificial neural
networks, precisely the Radial Basis Functions (RBF) neural
networks. The obtained simulation results show a satisfactory
agreement between responses of the developed neural model and the
experimental data for various letters and forms then the efficiency of
the proposed approaches.
Abstract: This study aimed at investigating whether the
functional brain networks constructed using the initial EEG (obtained
when patients first visited hospital) can be correlated with the
progression of cognitive decline calculated as the changes of
mini-mental state examination (MMSE) scores between the latest and
initial examinations. We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions, and the network analysis based
on graph theory to investigate the organization of functional networks
in aMCI. Our finding suggested that higher integrated functional
network with sufficient connection strengths, dense connection
between local regions, and high network efficiency in processing
information at the initial stage may result in a better prognosis of the
subsequent cognitive functions for aMCI. In conclusion, the functional
connectivity can be a useful biomarker to assist in prediction of
cognitive declines in aMCI.
Abstract: Brain functional networks based on resting-state EEG
data were compared between patients with mild Alzheimer’s disease
(mAD) and matched patients with amnestic subtype of mild cognitive
impairment (aMCI). We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions and the network analysis based
on graph theory to further investigate the alterations of functional
networks in mAD compared with aMCI group. We aimed at
investigating the changes of network integrity, local clustering,
information processing efficiency, and fault tolerance in mAD brain
networks for different frequency bands based on several topological
properties, including degree, strength, clustering coefficient, shortest
path length, and efficiency. Results showed that the disruptions of
network integrity and reductions of network efficiency in mAD
characterized by lower degree, decreased clustering coefficient, higher
shortest path length, and reduced global and local efficiencies in the
delta, theta, beta2, and gamma bands were evident. The significant
changes in network organization can be used in assisting
discrimination of mAD from aMCI in clinical.
Abstract: In healthy humans, the cortical brain rhythm shows
specific mu (~6-14 Hz) and beta (~18-24 Hz) band patterns in the
cases of both real and imaginary motor movements. As cerebellar
ataxia is associated with impairment of precise motor movement
control as well as motor imagery, ataxia is an ideal model system in
which to study the role of the cerebellocortical circuit in rhythm
control. We hypothesize that the EEG characteristics of ataxic patients
differ from those of controls during the performance of a
Brain-Computer Interface (BCI) task. Ataxia and control subjects
showed a similar distribution of mu power during cued relaxation.
During cued motor imagery, however, the ataxia group showed
significant spatial distribution of the response, while the control group
showed the expected decrease in mu-band power (localized to the
motor cortex).
Abstract: Analysis of EEG brainwave provides information on mental or emotional states. One of the particular states that can have various applications in human machine interface (HMI) is concentration. 8-channel EEG signals were measured and analyzed. The concentration index was compared during resting and concentrating periods. Among eight channels, locations the frontal lobe (Fp1 and Fp2) showed a clear increase of the concentration index during concentration regardless of subjects. The rest six channels produced conflicting observations depending on subjects. At this time, it is not clear whether individual difference or how to concentrate made these results for the rest six channels. Nevertheless, it is expected that Fp1 and Fp2 are promising locations for extracting control signal for HMI applications.
Abstract: Analyzing brain signals of the patients suffering from the state of depression may lead to interesting observations in the signal parameters that is quite different from a normal control. The present study adopts two different methods: Time frequency domain and nonlinear method for the analysis of EEG signals acquired from depression patients and age and sex matched normal controls. The time frequency domain analysis is realized using wavelet entropy and approximate entropy is employed for the nonlinear method of analysis. The ability of the signal processing technique and the nonlinear method in differentiating the physiological aspects of the brain state are revealed using Wavelet entropy and Approximate entropy.
Abstract: In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.