Full Research Paper
Bioinformatics / Biomedical Informatics / Medical Informatics / Health Informatics
Amin Janghorbani; Mohammad Hasan Moradi
Volume 10, Issue 3 , October 2016, Pages 197-209
Abstract
Babies are born under 2,500 g., defined as low birth weight (LBW) babies. They are exposed to the higher risks of mortality, congenital malformations, mental retardation, and other physical and neurological impairments. 15.5 % of births around the world are LBW. Reduction of the rate of LBW births to ...
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Babies are born under 2,500 g., defined as low birth weight (LBW) babies. They are exposed to the higher risks of mortality, congenital malformations, mental retardation, and other physical and neurological impairments. 15.5 % of births around the world are LBW. Reduction of the rate of LBW births to one-third is one of the aims of United Nations Children’s Fund program. Prognosis of LBW births can play a critical role in the reduction of these cases. Also, it helps clinicians to make timely and efficient clinical decisions to save these babies' life. In this study, a hybrid framework called fuzzy evidential network with a good ability to manage different aspects of uncertainty is a selected as the LBW prognosis model. The accuracy of prognosis and the performance of the fuzzy evidential network in the management of missing values of the clinical database were investigated and compared with well-known prognosis models of LBW. The results showed that the fuzzy evidential network has higher prognosis accuracy (84.8%) than other prognosis models. On the other hand, the fusion of naïve Bayes and the fuzzy evidential network outputs resulted in higher prognosis accuracy (85.2%). In addition, the fuzzy evidential network performance in the management of uncertainty induced by imputation method, was better than other prognosis models of this study. The performance loss of this framework as the results of the missing data increment, is less than other models.
Full Research Paper
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Zahra Tabanfar; Seyed Mohammad Firouzabadi; Zeynab Shankaei; Giv Sharifi; Kambiz Novin; Anahita Zoghi
Volume 10, Issue 3 , October 2016, Pages 211-221
Abstract
In this research, we analyzed the EEG signals of patients with brain tumor and healthy participants in order to study the effects of brain tumor on brain signals and also the feasibility of brain tumor detection using EEG signals. For this reason, EEG signals of four channel F3, F4, T3 and T4 from 5 ...
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In this research, we analyzed the EEG signals of patients with brain tumor and healthy participants in order to study the effects of brain tumor on brain signals and also the feasibility of brain tumor detection using EEG signals. For this reason, EEG signals of four channel F3, F4, T3 and T4 from 5 patients with brain tumor and 4 healthy participants were recorded. After preprocessing, linear features in time and frequency domains and nonlinear ones such as fractal dimensions and entropies were extracted. Afterwards, the differentiation between2 groups was analyzed using Davies-Bouldin Index, LDA, KNN and SVM classifiers. According to the results of Davies-Bouldin Index, RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed and RMS and Theta Absolute Power features in resting state with eyes opened, had the most distinction between the two groups. In this stage classification of two groups using single features was done and the most accuracy of 88.89% was obtained for RMS feature in resting state with eyes closed. At the end, classification of two groups using all selected features was conducted and the maximum accuracy of 82.54% was obtained for RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed. According to the results, EEG linear features have a good capability of detecting brain tumor. As these features are simple and have low computational complexity, they can be used in online applications especially for periodic screening tests.
Full Research Paper
Fluid-Structure Interaction in Biological Media / FSI
Hoda Mastari Farahani; Nasser Fatouraee
Volume 10, Issue 3 , October 2016, Pages 223-230
Abstract
Syrinx growth in Syringomyelia desease causes progressive neurological disorders. Thus, the examination of effective factors in syrinx development is so important for controlling this desease. One of clinical assumptions related to the reason of syrinx development, considers the propagation of pressure ...
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Syrinx growth in Syringomyelia desease causes progressive neurological disorders. Thus, the examination of effective factors in syrinx development is so important for controlling this desease. One of clinical assumptions related to the reason of syrinx development, considers the propagation of pressure wave shock in subarachnoid-space fluid as the main reason for fluid motion in syrinx and syrinx development and increasing damage to spinal cord. Modeling and analysis have been performed to test the theory in this research using finite element method. So a 3d model was created including syrinx, spinal cord, cerebrospinal-fluid in subarachnoid-space, dura mater and stenosis. Pressure puls stimulation was applied to the superior surface of the subarachnoid-space fluid model simulating arterial puls of skull. Cerebrospinal-fluid has been assumed as a Newtonian fluid with laminar flow. The solid phase has been considered to be linear elastic. The fluid-solid interface was analized using ADINA software and fluid flow characteristics were extracted including velocity and pressure field as well as tissue stresses. Results show that pressure wave propagation in subarachnoid-space fluid causes the induction of motion in syrinx fluid, and stress concentration is created in spinal tissue due to the fluid cessation in syrinx and increasing local pressure, however these stress values are lower than spinal tissue strength and pressure wave propagation in this situation cannot be the main reason of syrinx development.
Full Research Paper
Medical Instrumentation
Mohammad Saeed Zare Dehabadi; Mehran Jahed
Volume 10, Issue 3 , October 2016, Pages 231-244
Abstract
Wireless Body Area Networks (WBAN) consist of a collection of biosensors utilized to remotely monitor the health status of patients. High accuracy anomaly detection and distinguishing between faults and physiological anomalies play a key role in proper detection of real emergency situations and is cruicial ...
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Wireless Body Area Networks (WBAN) consist of a collection of biosensors utilized to remotely monitor the health status of patients. High accuracy anomaly detection and distinguishing between faults and physiological anomalies play a key role in proper detection of real emergency situations and is cruicial in lowering False Alarm Rate (FAR) cases. In this work, a univariate, unsupervised and real-time anomaly detection algorithm is proposed based on Hampel identifier and its performance is compared with previous and reported methods. Furthermore, a novel prediction method is introduced and utilized in order to correct for transient faults that are quite probable in WBANs, due to inherent noise and artifact of physiological sensors. Proposed method is shown to be faster than reported approaches while providing comparable. Final validation of the proposed method is performed by a real experimental dataset along with intentionally added faults and physiological anomalies. The results illustrate appropriate anomaly detection ability of the proposed approach.
Full Research Paper
Bioheat Transfer
Farshad Bahramian; Afsaneh Mojra
Volume 10, Issue 3 , October 2016, Pages 245-256
Abstract
The aim of this study is to investigate the use of thermography technique for detection of thyroid gland embedded in the neck through a numerical and an experimental approach. To this end, a real 3D model of the human neck and its primary organs including trachea, thyroid gland, common carotid artery ...
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The aim of this study is to investigate the use of thermography technique for detection of thyroid gland embedded in the neck through a numerical and an experimental approach. To this end, a real 3D model of the human neck and its primary organs including trachea, thyroid gland, common carotid artery and internal jugular vein is constructed based on the computerized tomography (CT) scan images of a healthy case and a case of thyroid cancer. The model is used for analyzing bio-heat transfer in the neck. In the thermal analysis the thyroid gland is considered as a heat source via specific function that generates heat based on the thyroid temporal temperature. Moreover, external convection through the neck skin surface and the ambient air, an internal convection through the inner layer of trachea and breathed air and heat transfer through the artery and the vein are considered. The result is the temperature distribution (thermogram) on the skin surface of the neck which reveals an approximate 0.5 -1.4 ˚C temperature increase on the area above thyroid gland for the healthy case. Studying effects of the thyroid cancer on the thermogram shows an approximate 0.7 -1.6 ˚C temperature increase due to the increased metabolic rate of the cancerous tumor compared to the healthy tissue. In order to practically investigate the applicability of thermography technique, a healthy case is examined by a high precision thermographic camera in similar conditions to the numerical simulation. Similar temperature increase due to the existence of the thyroid gland by the simulation and experiment affirmed the capability of the thermography method in the thyroid gland detection on the skin surface of the neck.
Full Research Paper
Cell Biomechanics / Cell Mechanics / Mechanobiology
sajad ghazavi; Bahman Vahidi
Volume 10, Issue 3 , October 2016, Pages 257-266
Abstract
Due to the importance of the brain and neurons, a vast area of research has been conducted in this field. However, due to the complexity of the neural behavior, each study investigated the functionality of neurons from one perspective such as electrophysiological, chemical, or mechanical perspective. ...
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Due to the importance of the brain and neurons, a vast area of research has been conducted in this field. However, due to the complexity of the neural behavior, each study investigated the functionality of neurons from one perspective such as electrophysiological, chemical, or mechanical perspective. In spite of the large number of research conducted on the brain injury topic, there is no study investigating the interaction of the mechanical and electrical characteristics of the neurons and its effect on the cell functionality. Understating the interaction between the mechanical and electrical properties of a neuron will have a substantial effect on treating neurological diseases such as traumatic brain injury and improving treatment methods such as ultrasound. As a result, there is a vital need to simulate the effect of mechanical forces on the electrophysiological behavior of a neuron. This study is one of the few attempts to achieve this goal by taking into account the mechanosensitivity of ion channels which affects the action potentials. Our proposed comprehensive model is based on power law equation (fractional dashpot) for mechanical modeling, Hodgkin Huxley (HH) equation for electrophysiological model and recent experiments for combination of these two equations. Based on the model, the calculated strain from the power law equation affects the activation and inactivation of ion channels. By changing the activation and inactivation variable in the HH equation, we can evaluate the effect of strain and mechanical stimulation on neural function. The results reveal neuron functions’ deficiency during neuron mechanical damage. As a result, action potential signal’s amplitude reduces. This reduction in amplitude of the action potential may be reversible or irreversible based on the amount of damage (plastic deformation).
Full Research Paper
Reza Foodeh; Vahid Shalchyan; Mohammad Reza Daliri
Volume 10, Issue 3 , October 2016, Pages 267-277
Abstract
Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate ...
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Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate spatial patterns in the electroencephalogram (EEG) signals to discriminate different motor imagery classes. Before applying CSP, Usually EEG signals are filtered out in 8-30 Hz to capture event related desynchronization (ERD) specific frequency rhythms called mu and beta bands. However, this frequency band could be highly subject specific. Therefore, optimizing spectral and spatial filters jointly could improve the classification accuracy. In this paper, we proposed a novel learning algorithm to derive spatial and spectral filters simultaneously using an evolutionary learning algorithm called particle swarm optimization (PSO). Furthermore, we utilized mutual information between extracted features and class labels as a cost function in the learning algorithm. Our simulations on BCI competition IV, dataset 1 reveals that the proposed method significantly outperforms the conventional CSP and filter bank CSP (FBCSP) with two different filter bank architectures.
