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ISRN Biomedical Engineering    [4 followers]  Follow    
  This is an Open Access Journal Open Access journal
     ISSN (Online) 2314-6346
     Published by ISRN International Scholarly Research Network Homepage  [96 journals]
  • Mathematical Methods in Biomedical Optics
    • Abstract: This paper presents a review of the phenomena regarding light-tissue interactions, especially absorption and scattering. The most important mathematical approaches for modeling the light transport in tissues and their domain of application: “first-order scattering,” “Kubelka-Munk theory,” “diffusion approximation,” “Monte Carlo simulation,” “inverse adding-doubling” and “finite element method” are briefly described.
      PubDate: Mon, 30 Dec 2013 16:09:19 +000
  • Teager Energy Based Filter-Bank Cepstra in EEG Classification for Seizure
           Detection Using Radial Basis Function Neural Network
    • Abstract: About 1–3% of the world population suffers from epilepsy. Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalograph (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through static and dynamic features derived from three Teager energy based filter-bank cepstra (TE-FB-CEPs). We compared the performance of linear, logarithmic, and Mel frequency scale TE-FB-CEPs using radial basis function neural network in general epileptic seizure detection. The comparison is tried on eight different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In a previous study, using traditional cepstrum on the same database, we had found that the composite vectors showed a degraded performance in seizure detection. In this study, however, irrespective of frequency scaling used, it is found that the composite vectors of TE-FB-CEPs maintain excellent overall accuracy in all the eight classification problems.
      PubDate: Mon, 25 Nov 2013 17:07:07 +000
  • Optical Measurement of Blood Oxygen Saturation of Dental Pulp
    • Abstract: The applicability of arterial pulse oximetry to dental pulp was demonstrated using in vitro and in vivo measurements. First, porcine blood of known oxygen saturation (SO2) was circulated through extracted human upper incisors, while transmitted-light plethysmography was performed using three different visible wavelengths. From the light intensity waveforms measured in vitro, a parameter that is statistically correlated to SO2 was calculated using the pulsatile/nonpulsatile component ratios of two wavelengths for different SO2. Then, values were measured in vivo for living incisors, and the corresponding SO2 values were calculated using the results of in vitro measurements. The estimated SO2 values of the upper central incisors measured in vivo were from 71.0 to 92.7%. This study showed the potential to measure the oxygen saturation changes to identify the sign of pulpal inflammation.
      PubDate: Wed, 06 Nov 2013 15:56:29 +000
  • Comparison of Baseline Cepstral Vector and Composite Vectors in the
           Automatic Seizure Detection Using Probabilistic Neural Networks
    • Abstract: Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalogram (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through traditional cepstrum and the cepstrum-derived dynamic features. We compared the performance of the traditional baseline cepstral vector with that of the two composite vectors, the first including velocity cepstral coefficients and the second including velocity and acceleration cepstral coefficients, using probabilistic neural network in general epileptic seizure detection. The comparison is tried on seven different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In this study, it is found that the overall performance of both the composite vectors deteriorates compared to that of baseline cepstral vector.
      PubDate: Tue, 27 Aug 2013 08:51:03 +000
  • Slip Effects on Pulsatile Flow of Blood through a Stenosed Arterial
           Segment under Periodic Body Acceleration
    • Abstract: A theoretical investigation concerning the influence of externally imposed periodic body acceleration on the flow of blood through a time-dependent stenosed arterial segment by taking into account the slip velocity at the wall of the artery has been carried out. A mathematical model is developed by treating blood as a non-Newtonian fluid obeying the Casson fluid model. The pulsatile flow is analyzed by considering a periodic pressure gradient and the inertial effects as negligibly small. A suitable generalized geometry for time-dependent stenosis is taken into account. Perturbation method is used to solve the coupled implicit system of nonlinear differential equations that govern the flow of blood. Analytical expressions for the velocity profile, volumetric flow rate, and wall shear stress are obtained. A thorough quantitative analysis has been made through numerical computations of the variables involved in the analysis that are of special interest in this study. The computational results are presented graphically. The results for different values of the parameters involved in the problem under consideration presented here show that the flow is appreciably influenced by slip velocity in the presence of periodic body acceleration.
      PubDate: Sun, 18 Aug 2013 12:06:50 +000
  • High-Fidelity Visualization of Large Medical Datasets on Commodity
    • Abstract: Recent advances in CT and MRI static and dynamic scanning techniques have led to great improvements in the resolution and size of volumetric medical datasets, and this trend is still ongoing. However, the explosion of dataset size prevents clinicians from taking advantage of an interactive, high-resolution exploration of volumetric medical data on commodity hardware, due to the memory constraints of modern graphics cards. This paper presents a hybrid CPU-GPU volume ray-casting method and some hybrid-based inspection tools aimed at providing interactive, medical-quality visualization using an ordinary desktop PC. Experimental results show that the hybrid method provides a near-interactive high-fidelity visualization of large medical datasets even if only limited hardware resources are available.
      PubDate: Thu, 27 Jun 2013 10:50:24 +000
  • 0.5 V Cardiac Sense Amplifier Realization Using Log-Domain
    • Abstract: A novel configuration of a cardiac sense amplifier for pacemakers, realized using the concept of Log-Domain filtering, is introduced in this paper. The analog part of the amplifier operates under a single 0.5 V power supply voltage. Compared to the corresponding already published configuration, the proposed scheme offers the benefits of reduced operating voltage and dc power dissipation. The performance of the intermediate stages, as well as of the whole system, has been evaluated through the utilization of the Analog Design Environment of the Cadence software and, also, the design kit provided by the AMS 0.35 μm CMOS process.
      PubDate: Sun, 23 Jun 2013 14:58:32 +000
  • Companding Realizations of the Nonlinear Energy Operator
    • Abstract: Realizations of the nonlinear energy operator (NEO), using the concept of companding filtering, are introduced and compared in this work. For this purpose, the Log-Domain and Sinh-Domain filtering techniques have been followed. Both topologies are constructed from differentiator and multiplier blocks which have been realized through the utilization of nonlinear transconductor cells. Both of the proposed topologies offer the capability of ultra-low voltage operation, thanks to the employment of MOS transistors in the weak inversion. Considering a single power supply voltage of 0.5 V, the behavior of the proposed NEO realizations has been simulated using the Analog Design Environment of the Cadence software and the design kit of the TSMC 130 nm process. Comparison results show that the Sinh-Domain realization offers a more power efficient design than that offered by the Log-Domain realization.
      PubDate: Thu, 20 Jun 2013 17:16:06 +000
  • A New Approach to Detect Epileptic Seizures in Electroencephalograms Using
           Teager Energy
    • Abstract: A Teager energy (TE) based approach to discriminate electroencephalogram signals corresponding to nonseizure (eyes open, eyes closed, or interictal) and seizure (ictal) intervals is proposed. Though a good number of contributions have been made for seizure detection, the challenges of unbalanced data (nonseizure and seizure events) and system computational efficiency still remain a challenge. It is reported in the literature that the seizures are characterized by abnormal sudden discharges in the brain which get manifested in the EEG recordings by frequency changes and increased amplitudes. Teager energy (TE) is capable of tracking such rapid changes in frequency as well as amplitude in the time domain. An important finding of this study is that the mean TE quantifier is largely independent of the window length and exhibits relative consistency when used as a relative measure for comparison. We compared the diagnostic capability of TE quantifier with those of Higuchi’s fractal dimension and sample entropy in discriminating nonseizure and seizure states in the EEGs and found that TE outperforms the other two nonlinear quantifiers. The result shows that the application of this method compares favorably with conventional classification methods in terms of performance and is well suited for real-time automatic epileptic seizure detection.
      PubDate: Wed, 19 Jun 2013 15:12:19 +000
  • Artificial Neural Network-Based Automated ECG Signal Classifier
    • Abstract: The ECG signal is well known for its nonlinear dynamic behavior and a key characteristic that is utilized in this research; the nonlinear component of its dynamics changes more significantly between normal and abnormal conditions than does the linear one. As the higher-order statistics (HOS) preserve phase information, this study makes use of one-dimensional slices from the higher-order spectral domain of normal and ischemic subjects. A feedforward multilayer neural network (NN) with error back-propagation (BP) learning algorithm was used as an automated ECG classifier to investigate the possibility of recognizing ischemic heart disease from normal ECG signals. Different NN structures are tested using two data sets extracted from polyspectrum slices and polycoherence indices of the ECG signals. ECG signals from the MIT/BIH CD-ROM, the Normal Sinus Rhythm Database (NSR-DB), and European ST-T database have been utilized in this paper. The best classification rates obtained are 93% and 91.9% using EDBD learning rule with two hidden layers for the first structure and one hidden layer for the second structure, respectively. The results successfully showed that the presented NN-based classifier can be used for diagnosis of ischemic heart disease.
      PubDate: Mon, 17 Jun 2013 18:51:34 +000
  • Ambulatory Monitoring of Physical Activity Based on Knee Flexion/Extension
           Measured by Inductive Sensor Technology
    • Abstract: We developed a knee brace to measure the knee angle and implicitly the flexion/extension (f/e) of the knee joint during daily activities. The goal of this study is to classify and validate a limited set of physical activities on ten young healthy subjects based on knee f/e. Physical activities included in this study are walking, ascending and descending of stairs, and fast locomotion (such as jogging, running, and sprinting) at self-selected speeds. The knee brace includes 2 accelerometers for static measurements and calibration and an inductive sensor for dynamic measurements. As we focus on physical activities, the inductive sensor will provide the required information on knee f/e. In this study, the subjects traversed a predefined track which consisted of indoor paths, outdoor paths, and obstacles. The activity classification algorithm based on peak detection in the knee f/e angle resulted in a detection rate of 95.9% for walking, 90.3% for ascending stairs, 78.3% for descending stairs, and 82.2% for fast locomotion. We conclude that we developed a measurement device which allows long-term and ambulatory monitoring. Furthermore, it is possible to predict the aforementioned activities with an acceptable performance.
      PubDate: Sun, 09 Jun 2013 14:21:14 +000
  • Lossless Medical Image Compression by Integer Wavelet and Predictive
    • Abstract: The future of healthcare delivery systems and telemedical applications will undergo a radical change due to the developments in wearable technologies, medical sensors, mobile computing, and communication techniques. When dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis we required to considered many parameters for telemedical application. E-health was born with the integration of networks and telecommunications. In recent years, healthcare systems rely on images acquired in two-dimensional domains in the case of still images or three-dimensional domains for volumetric video sequences and images. Images are acquired by many modalities including X-ray, magnetic resonance imaging, ultrasound, positron emission tomography, and computed axial tomography (Sapkal and Bairagi, 2011). Medical information is either in multidimensional or multiresolution form, which creates enormous amount of data. Retrieval, efficient storage, management, and transmission of these voluminous data are highly complex. One of the solutions to reduce this complex problem is to compress the medical data without any loss (i.e., lossless). Since the diagnostics capabilities are not compromised, this technique combines integer transforms and predictive coding to enhance the performance of lossless compression. The proposed techniques can be evaluated for performance using compression quality measures.
      PubDate: Tue, 04 Jun 2013 12:32:09 +000
  • Extrahepatic 25-Hydroxylation of Vitamin D3 in an Engineered Osteoblast
           Precursor Cell Line Exploring the Influence on Cellular Proliferation and
           Matrix Maturation during Bone Development
    • Abstract: Osteoblastic precursors experience distinct stages during differentiation and bone development, which include proliferation, extracellular matrix (ECM) maturation, and ECM mineralization. It is well known that vitamin D plays a large role in the regulation of bone mineralization and homeostasis via the endocrine system. The activation of vitamin D requires two sequential hydroxylation steps, first in the kidney and then in the liver, in order to carry out its role in calcium homeostasis. Recent research has demonstrated that human-derived mesenchymal stem cells (MSCs) and osteoblasts can metabolize the immediate vitamin D precursor 25-dihydroxyvitamin D3 (25OHD3) to the active steroid 1α,25-dihydroxyvitamin D3 (1,25OH2D3) and elicit an osteogenic response. However, reports of extrahepatic metabolism of vitamin D3, the parental vitamin D precursor, have been limited. In this study, we investigated whether osteoblast precursors have the capacity to convert vitamin D3 to 1,25OH2D3 and examined the potential of vitamin D3 to induce 1,25OH2D3 associated biological activities in osteoblast precursors. It was demonstrated that the engineered osteoblast precursor derived from human marrow (OPC1) is capable of metabolizing vitamin D3 to 1,25OH2D3 in a dose-dependent manner. It was also demonstrated that administration of vitamin D3 leads to the increase in alkaline phosphatase (ALP) activity associated with osteoblast ECM maturation and calcium deposits and a decrease in cellular proliferation in both osteoblast precursor cell lines OPC1 and MC3T3-E1. These findings provide a two-dimensional culture foundation for future three-dimensional engineered tissue studies using the OPC1 cell line.
      PubDate: Tue, 04 Jun 2013 09:53:45 +000
  • Diffusion in Replica Healthy and Emphysematous Alveolar Models Using
           Computational Fluid Dynamics
    • Abstract: Deposition of nanosized particles in the pulmonary region has the potential of crossing the blood-gas barrier. Experimental in vivo studies have used micron-sized particles, and therefore nanoparticle deposition in the pulmonary region is not well understood. Furthermore, little attention has been paid to the emphysematous lungs, which have characteristics quite different from the healthy lung. Healthy and emphysematous replica acinus models were created from healthy and diseased human lung casts using three-dimensional reconstruction. Particle concentration and deposition were determined by solving the convective-diffusion equation numerically for steady and unsteady cases. Results showed decreased deposition efficiencies for emphysema compared to healthy lungs, consistent with the literature and attributed to significant airway remodeling in the diseased lung. Particle diffusion was found to be six times slower in emphysema compared to healthy model. The unsteady state simulation predicted deposition efficiencies of 96% in the healthy model for the 1 nm and 3 nm particles and 94% and 93% in the emphysema model for the 1 nm and 3 nm particles, respectively. Steady state was achieved in less than one second for both models. Comparisons between steady and unsteady predictions indicate that a steady-state simulation is reasonable for predicting particle transport under similar conditions.
      PubDate: Mon, 03 Jun 2013 18:21:34 +000
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