Abstract: The medical data statistical analysis often requires the
using of some special techniques, because of the particularities of
these data. The principal components analysis and the data clustering
are two statistical methods for data mining very useful in the medical
field, the first one as a method to decrease the number of studied
parameters, and the second one as a method to analyze the
connections between diagnosis and the data about the patient-s
condition. In this paper we investigate the implications obtained from
a specific data analysis technique: the data clustering preceded by a
selection of the most relevant parameters, made using the principal
components analysis. Our assumption was that, using the principal
components analysis before data clustering - in order to select and to
classify only the most relevant parameters – the accuracy of
clustering is improved, but the practical results showed the opposite
fact: the clustering accuracy decreases, with a percentage
approximately equal with the percentage of information loss reported
by the principal components analysis.
Abstract: This paper systematically investigates the timedependent
health outcomes for office staff during computer work
using the developed mathematical model. The model describes timedependent
health outcomes in multiple body regions associated with
computer usage. The association is explicitly presented with a doseresponse
relationship which is parametrized by body region
parameters. Using the developed model we perform extensive
investigations of the health outcomes statically and dynamically. We
compare the risk body regions and provide various severity rankings
of the discomfort rate changes with respect to computer-related
workload dynamically for the study population. Application of the
developed model reveals a wide range of findings. Such broad
spectrum of investigations in a single report literature is lacking.
Based upon the model analysis, it is discovered that the highest
average severity level of the discomfort exists in neck, shoulder, eyes,
shoulder joint/upper arm, upper back, low back and head etc. The
biggest weekly changes of discomfort rates are in eyes, neck, head,
shoulder, shoulder joint/upper arm and upper back etc. The fastest
discomfort rate is found in neck, followed by shoulder, eyes, head,
shoulder joint/upper arm and upper back etc. Most of our findings are
consistent with the literature, which demonstrates that the developed
model and results are applicable and valuable and can be utilized to
assess correlation between the amount of computer-related workload
and health risk.
Abstract: Many studies have emphasized the importance of
resistive exercise to maintain a healthy human body, particular in
prevention of weakening of physical strength. Recently, some studies
advocated that an application of vibration as a supplementary means in
a regular training was effective in encouraging physical strength. Aim
of the current study was, therefore, to identify if an application of
vibration in a resistive exercise was effective in encouraging physical
strength as that in a regular training. A 3-dimensional virtual lower
extremity model for a healthy male and virtual leg-press model were
generated and synchronized. Dynamic leg-press exercises on a slide
machine with/without extra load and on a footboard with vibration as
well as on a slide machine with extra load were analyzed. The results
of the current indicated that the application of the vibration on the
dynamic leg-press exercise might be not greatly effective in
encouraging physical strength, compared with the dynamic leg press
exercise with extra load. It was, however, thought that the application
of the vibration might be helpful to elderly individuals because the
reduced maximum muscle strength appeared by the effect of the
vibration may avoid a muscular spasm, which can be driven from a
high muscle strength sometimes produced during the leg-press
exercise with extra load.
Abstract: Many studies have focused on the nonlinear analysis
of electroencephalography (EEG) mainly for the characterization of
epileptic brain states. It is assumed that at least two states of the
epileptic brain are possible: the interictal state characterized by a
normal apparently random, steady-state EEG ongoing activity; and
the ictal state that is characterized by paroxysmal occurrence of
synchronous oscillations and is generally called in neurology, a
seizure.
The spatial and temporal dynamics of the epileptogenic process is
still not clear completely especially the most challenging aspects of
epileptology which is the anticipation of the seizure. Despite all the
efforts we still don-t know how and when and why the seizure
occurs. However actual studies bring strong evidence that the
interictal-ictal state transition is not an abrupt phenomena. Findings
also indicate that it is possible to detect a preseizure phase.
Our approach is to use the neural network tool to detect interictal
states and to predict from those states the upcoming seizure ( ictal
state). Analysis of the EEG signal based on neural networks is used
for the classification of EEG as either seizure or non-seizure. By
applying prediction methods it will be possible to predict the
upcoming seizure from non-seizure EEG.
We will study the patients admitted to the epilepsy monitoring
unit for the purpose of recording their seizures. Preictal, ictal, and
post ictal EEG recordings are available on such patients for analysis
The system will be induced by taking a body of samples then
validate it using another. Distinct from the two first ones a third body
of samples is taken to test the network for the achievement of
optimum prediction. Several methods will be tried 'Backpropagation
ANN' and 'RBF'.
Abstract: Hepatocellular carcinoma, also called hepatoma, most
commonly appears in a patient with chronic viral hepatitis. In
patients with a higher suspicion of HCC, such as small or subtle
rising of serum enzymes levels, the best method of diagnosis
involves a CT scan of the abdomen, but only at high cost. The aim of
this study was to increase the ability of the physician to early detect
HCC, using a probabilistic neural network-based approach, in order
to save time and hospital resources.
Abstract: TELMES project aims to develop a securized
multimedia system devoted to medical consultation teleservices. It
will be finalized with a pilot system for a regional telecenters
network that connects local telecenters, having as support
multimedia platforms. This network will enable the implementation
of complex medical teleservices (teleconsulations, telemonitoring,
homecare, urgency medicine, etc.) for a broader range of patients
and medical professionals, mainly for family doctors and those
people living in rural or isolated regions. Thus, a multimedia,
scalable network, based on modern IT&C paradigms, will result. It
will gather two inter-connected regional telecenters, in Iaşi and
Piteşti, Romania, each of them also permitting local connections of
hospitals, diagnostic and treatment centers, as well as local networks
of family doctors, patients, even educational entities. As
communications infrastructure, we aim to develop a combined fixmobile-
internet (broadband) links. Other possible communication
environments will be GSM/GPRS/3G and radio waves. The
electrocardiogram (ECG) acquisition, internet transmission and
local analysis, using embedded technologies, was already
successfully done for patients- telemonitoring.
Abstract: This study focuses on examining why the range of
experience with respect to HIV infection is so diverse, especially in
regard to the latency period. An agent-based approach in modelling
the infection is used to extract high-level behaviour which cannot be
obtained analytically from the set of interaction rules at the cellular
level. A prototype model encompasses local variation in baseline
properties, contributing to the individual disease experience, and is
included in a network which mimics the chain of lymph nodes. The
model also accounts for stochastic events such as viral mutations.
The size and complexity of the model require major computational
effort and parallelisation methods are used.