|
|
|
Issue 1, Volume 3,
January 2008
Title of the Paper: Motor Control Information Extracted from Surface EMG as Muscle Force
Estimation
DOWNLOAD FULL PDF
Authors: Rok Istenic, Ales Holobar, Marco Gazzoni, Damjan Zazula
Abstract: The aim of this paper is to introduce an extension to a force estimation technique based on activity index and
to compare it to two other muscle force estimation techniques that also use the motor control information on the same
set of surface EMG signals. Our new method is called motor unit twitch force technique and the compared methods
include motor unit action potential rate and activity index. The main difference of the three compared methods lies in
the extraction of the motor control information from multi-channel surface EMG. Motor unit action potential rate and
activity index measure global muscle activity as they represent the summation of innervation pulse trains of all active
motor units, while twitch force technique decomposes the surface EMG and obtains the activity of all active individual
motor units separately. This means a great improvement over activity index and motor unit action potential rate
methods as both force regulation principles, i.e. motor unit recruitment and firing rate modulation can be observed.
Surface EMG signals used in the experiment were recorded from biceps brachii muscle during elbow flexion on five
subjects. Two-dimensional matrix of surface electrodes (13 rows by 5 columns) was applied. Isometric constant force
contractions at three different force levels were performed, i.e. at 5, 10 and 30 % of maximal voluntary contraction.
Torque produced at the elbow joint was measured simultaneously with surface EMG. The force estimation error of the
methods was measured by root mean square error between the recorded and estimated force. Our new motor unit twitch
force technique reduced the muscle force estimation error significantly, for 13% when compared to the motor unit
action potential rate, and for 2% when compared to the activity index method.
Keywords: surface electromyography, muscle force estimation, EMG force relationship, MUAP rate, activity index,
twitch force
Title of the Paper: Agreement Between Multi-Layer Perceptron and a Compound Neural
Network on ECG Diagnosis of Aatrioventricular Blocks
DOWNLOAD FULL PDF
Authors: Salama Meghriche, Mohammed Boulemden and Amer Draa
Abstract: Artificial Neural Networks (ANN) are computer-based expert systems that have proved to be useful
in pattern recognition tasks. ANN can be used in different phases of the decision-making process, from
classification to diagnostic procedures. In this work, we develop two methods. The first one based on a
compound neural network (CNN) composed of three different multilayer neural networks of the feed forward
type, and the second one based on only a multi-layer perceptron (MLP). Such both of them have the capability
to classify electrocardiograms (ECG) as normal or as carrying atrioventricular blocks (AVB). These networks
were fed with same measurements from one lead of the ECG. A single output unit encodes the probability of
AVB occurrences. The difference in performance between the two neural networks classifiers was measured as
the difference in area under the receiver operating characteristic curves (ROC). The results show that the CNN
and MLP have a good performance in detecting AVBs.
Keywords: Artificial neural networks, Biomedical data, Electrocardiogram (ECG), Medical diagnosis,
Pattern recognition, Signal processing,
Issue 2, Volume 3, February 2008
Title of the Paper: Minimization of Tumor Volume and Endothelial Support
for a System Describing Tumor Anti-Angiogenesis
DOWNLOAD FULL PDF
Authors: Urszula Ledzewicz, Heinz Schattler
Abstract: Anti-angiogenic therapy is a novel treatment approach for cancer that aims at preventing a tumor from
developing its own network of blood vessels that it needs for its supply of nutrients and thus indirectly inhibits
the growth of the tumor. In this paper a mathematical model for anti-angiogenic treatment is analyzed as a 3-
dimensional optimal control problem with the aim of minimizing a convex combination of tumor volume and
endothelial support. The latter represents a measure for the size of the tumor’s vasculature. The results are compared
with the solutions for the problem when only the tumor volume is minimized.
Keywords: Optimal control, singular controls, cancer treatments, tumor growth, anti-angiogenesis
Title of the Paper: Random Modeling of Population Dynamics with Uncertainty
DOWNLOAD FULL PDF
Authors: Gilberto Gonzalez, LucasJodar,
RafaelVillanueva, FransiscoSantonja
Abstract: Obesity is growing at an important rate in developed and developing countries and it is becoming a
serious disease not only from the individual health point of view but also from the public socioeconomic one. In
this paper it is studied the effect of uncertainty in the dynamics behavior of the overweight and obesity childhood
populations. Since initial conditions and parameters appearing in a deterministic mathematical model of obesity
population are subject to some degree of uncertainty, randomness in the differential equations are introduced in
the initial conditions and in the most relevant parameter of the deterministic model. Additionally, in this work
stochastic and random ordinary differential equations were used to study the randomness effect in the deterministic
mathematical model of obesity population. Monte Carlo simulations are performed assuming different distributions
for the initial conditions and parameters of the model. Furthermore, confidence intervals and expected solutions
of the random models are also obtained. To verify the consistence of the method, results are compared against
numerical solutions of the deterministic mathematical model.
Keywords: Random differential equation, Population dynamics, Numerical simulation, Stochastic differential
equation, Monte Carlo method.
Issue 3, Volume 3, March 2008
Title of the Paper: Minimizing the Set Up for ADL Monitoring through DTW Hierarchical Classification on Accelerometer Data
DOWNLOAD FULL PDF
Authors: Rossana Muscillo, Silvia Conforto,
Maurizio Schmid, Tommaso D’alessio
Abstract: Systems for remote monitoring of motor activities in the elderly are becoming very popular in developed countries. In this context, recognition and classification of Activities of Daily Living (ADL) is a very important step that can open intriguing scenarios, especially if real-time techniques become available. The present work proposes a hierarchical classifier based on the Dynamic Time Warping (DTW) technique, applied on data recorded from a tri-axial accelerometer placed on the shin, to classify among different motor activities. The classifier was applied to the recognition of walking, climbing and descending stairs of five different subjects. After the calibration phase needed to extract the templates, the technique makes it possible to recognize activities by determining the distance between the signal input and a set of the previously defined templates. Signals coming from the three different channels are used in a hierarchical way, with three layers. The hierarchy has been set based on sorting channels by signal to noise ratio in descending order. The results show a classification with overall percentage of error less than 5%.
Keywords: Wearable sensors, Accelerometer, ADL , Dynamic Time Warping, Template, Classification
Title of the Paper: Comparison of Cluster Identification Methods for Selection of GO Terms related to Gene Clusters
DOWNLOAD FULL PDF
Authors: Yoichi Yamada, Yuki Miyata,
Masanori Higashihara, Kenji Satou
Abstract: The hierarchical clustering algorithm has frequently been applied to grouping genes sharing a certain characteristic from a microarray data set. Identification of clusters from a hierarchical cluster tree is generally conducted by cutting the tree at a certain level. In this method, the most parental clusters are identified as mutually correlated gene groups and their sibling clusters are ignored. However the sibling clusters have a possibility to show more significant GO term annotation than their parental clusters. To overcome this problem, Toronen developed a novel algorithm based on the calculation of each GO annotation in all the clusters that satisfy a threshold of correlation distance. However comparison of the algorithm and the general method has not been done enough yet. Therefore we compared the general method with Toronen‟s proposed algorithm for identifying gene cluster-relevant GO terms. Moreover, we compared the hierarchical clustering with fuzzy k-means clustering which can group a object into more than one cluster and permit a object not to belong to any clusters. Consequently, we confirmed that Toronen‟s algorithm is more available for identification of gene clusters and their relevant GO terms from a microarray data set than the other methods.
Keywords: Hierarchical clustering, Gene Ontology, Microarray data, Yeast cell cycle
Issue 4, Volume 3, April 2008
Title of the Paper: Stabilizing Effect of Prey Competition for Predators Exhibiting
Switching Feeding Behavior
DOWNLOAD FULL PDF
Authors: Valerio Ajraldi, Ezio Venturino
Abstract: The classical model by Tanksy on a two-level food web with a predator feeding on two kinds of prey
is revisited and extended. The ecosystem with intraspecific and interspecific competition for resources among the
prey is analized. Two equilibria are found: a segment of conditionally (neutrally) stable equilibrium points and
the interior coexistence equilibrium, which is proven to be inconditionally stable. The predator population settles
to a lower level than the one arising in the original Tansky’s model. In addition, there is inverse proportionality
between the predators’ mortality and the equilibrium value. Predators’ recovery and the settling of the system
toward coexistence are also allowed by a large prey carrying capacity.
Keywords: Predator-prey, switching mechanism, Tansky model, competition, stable equilibria
Title of the Paper: Blood Glucose Data Processing
for Automated Diagnosis
DOWNLOAD FULL PDF
Authors: Eugen Iancu, Ionela Iancu, Maria Moţa
Abstract: The actual protocols used in diagnosis and management of the diabetes mellitus include the classical
clinical trials and the physicians’ experience, but they do not account by the dynamics of the blood glucose and
insulin. So, it is natural to have many diabetes patients with poor control of blood glucose values. The
introduction in the medical practice of the blood glucose continuous monitoring systems has made possible the
automated analyse of blood glucose dynamics. Along this paper the authors present algorithms for automatic
diagnosis in the diabetic patients monitoring with applications, especially in the intensive care units and
telemedicine. We have focused on the statistical analysis methods in order to detect the reliable characteristics,
useful in the identification of standard aspects or stable patterns for each type and stage of the complex and
long-term evolution of the disease that is diabetes mellitus. Examples of the frequency range of blood glucose
dynamics of normal subjects and subjects with diabetes are presented with the help of Wigner-Ville
distribution. The spectral analysis reveals the frequency band edge and offers the basic information to correct
determination of Nyquist sample period. These findings may have significant clinical implication in diagnosis
of the diabetes mellitus, in blood glucose monitoring and the management of the diabetes therapy.
Keywords: Statistical analysis, Diabetes mellitus, Continuous glucose monitoring, Probability distribution
function, Periodogram, Correlation
Issue 5, Volume 3, May 2008
Title of the Paper: Drug Resistants Impact on Tuberculosis Transmission
DOWNLOAD FULL PDF
Authors: Silvia Martorano Raimundo, Ezio Venturino
Abstract: In epidemiology, measures for prophilaxis of infectious diseases are taken often using mathematical
methods and statistical tools to evaluate possible future scenarios of the evolution of transmissible diseases. Here
we present two models for TB transitions among different stages of the disease. We analyze them assuming to have
a large basin of susceptible individuals available. The models account for immigration and demographic effects. A
flow of infected members into the population is assumed. Part of it is made by a specified fraction is drug-sensitive
latent individuals, while the other part consists of drug-resistant latent individuals. Our aim is the description
and analysis of all the possible ways the infected individuals move. In particular, from the classes of latent to
infectious, and possibly back upon successful treatment, or toward acute stages of the disease for drug resistant
cases due to improper, incomplete or ineffective healing measures. Some conditions for the eradication of the
disease are extracted from analytical considerations and simulation results and might be useful for epidemiological
implementations.
Keywords: Drug resistant, transmissible disease, tuberculosis.
Title of the Paper: Application of a Feature Selection Method to Nucleosome Data:
Accuracy Improvement and Comparison with Other Methods
DOWNLOAD FULL PDF
Authors: Masanori Higashihara, Jovan David Rebolledo-Mendez, Yoichi Yamada, Kenji Satou
Abstract: In binary classification problem, data of feature vectors with binary labels are prepared in general. However, today it is well known that using all the features for discrimination does not always the best way to achieve the highest accuracy in prediction. Feature selection is a technique to find a subset of features with the highest accuracy by eliminating features harmful in prediction. Among various methods proposed, in this study we used a method which can be divided in two steps. Firstly, along the ranked features f1,…,fn based on Gini index, the feature subsets {f1},{f1,f2},...,{f1,…,fn} are tested by SVM with RBF kernel. Secondary, variants of the best feature subset found in the first step are tested in the same way. In the application to the prediction of nucleosome occupancy and modification from genome subsequence, the method achieved a small but assured improvement from the previous study. In addition, observed ranking of features revealed some relationships between features and categories of nucleosome datasets. Finally, the method was compared with other promising methods and outperformed them.
Keywords: Epigenetics, Histone, Acetylation, Methylation, Feature selection, Support vector machine, Gini-index, Random forest
Issue 6, Volume 3, June 2008
Title of the Paper: A Fractal Approach to Pattern Formation in Biological Systems
DOWNLOAD FULL PDF
Authors: Radu Dobrescu, Loretta Ichim, Stefan Mocanu, Stefan Popa
Abstract: The paper discusses the connection between pattern formation and nonlinear dynamics, focusing on
the similarity between discrete patterns and fractal structures, and then describes different solutions to model
reaction-diffusion systems as representative processes in morphogenesis. The option for a discrete model and
the steps to design it as a fractal structure is argued. Construction of appropriate generic model is an important
step towards understanding the bacteria. It is shown how a pattern with arbitrary complexity like a fractal
pattern can be realized by a reaction-diffusion system. A specific example is the diffusion limited aggregation
growth process, illustrated by the simulation of the evolution of a bacterial colony that shows the roles of
instability and sensitivity in nonequilibrium pattern formation.
Keywords: morphogenesis, pattern formation, reaction-diffusion systems, fractal analysis, attractors,
diffusion limited aggregation
Title of the Paper: Stationary Densities and Parameter Estimation for Delayed Stochastic
Logistic Growth Laws with Application in Biomedical Studies
DOWNLOAD FULL PDF
Authors: Petras Rupšys
Abstract: The study of nonlinear stochastic delayed process is significant for understanding nature of complex
system in reductionistic viewpoints. This paper investigates the stochastic linear and logistic (Verhulst,
Gompertz and Richards) models, and simulates the growth process of Ehrilch ascities tumor (EAT) in a mouse.
In order to explain the oscillations of EAT growth we use a system of stochastic differential equations with
time delay. We derive the exact and approximate stationary densities in the case of small time delays. For the
estimation of parameters we propose the L1 distance and maximum likelihood procedures. As an illustrative
experience we use a real data set from repeated measurements on Ehrilch ascities tumor in a mouse. The results
are implemented in the symbolic computational language MAPLE.
Keywords: Ehrilch ascities tumor, Stochastic differential equation, Density function, Fokker-Plank equation,
Numerical solution.
|
|
|
