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Biomedical Engineering, IEEE Transactions on

Issue 6 • Date June 2012

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Displaying Results 1 - 25 of 36
  • [Front cover]

    Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Biomedical Engineering publication information

    Page(s): C2
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    Freely Available from IEEE
  • Table of contents

    Page(s): 1497 - 1498
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  • Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches

    Page(s): 1499 - 1506
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (619 KB) |  | HTML iconHTML  

    We present a real-time method for the detection of motion and noise (MN) artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN artifact detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the artifacts' dynamics as they are largely concentrated in the higher frequencies. The second stage of our approach uses three statistical measures on the F-IMF time series to look for characteristics of randomness and variability, which are hallmark signatures of MN artifacts: the Shannon entropy, mean, and variance. We then use the receiver-operator characteristics curve on Holter data from 15 healthy subjects to derive threshold values associated with these statistical measures to separate between the clean and MN artifacts' data segments. With threshold values derived from 15 training data sets, we tested our algorithms on 30 additional healthy subjects. Our results show that our algorithms are able to detect the presence of MN artifacts with sensitivity and specificity of 96.63% and 94.73%, respectively. In addition, when we applied our previously developed algorithm for atrial fibrillation (AF) detection on those segments that have been labeled to be free from MN artifacts, the specificity increased from 73.66% to 85.04% without loss of sensitivity (74.48%-74.62%) on six subjects diagnosed with AF. Finally, the computation time was less than 0.2 s using a MATLAB code, indicating that real-time application of the algorithms is possible for Holter monitoring. View full abstract»

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  • Biodegradable Microfabricated Plug-Filters for Glaucoma Drainage Devices

    Page(s): 1507 - 1513
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (696 KB) |  | HTML iconHTML  

    We report on the development of a batch fabricated biodegradable truncated-cone-shaped plug filter to overcome the postoperative hypotony in nonvalved glaucoma drainage devices. Plug filters are composed of biodegradable polymers that disappear once wound healing and bleb formation has progressed past the stage where hypotony from overfiltration may cause complications in the human eye. The biodegradable nature of device eliminates the risks associated with permanent valves that may become blocked or influence the aqueous fluid flow rate in the long term. The plug-filter geometry simplifies its integration with commercial shunts. Aqueous humor outflow regulation is achieved by controlling the diameter of a laser-drilled through-hole. The batch compatible fabrication involves a modified SU-8 molding to achieve truncated-cone-shaped pillars, polydimethylsiloxane micromolding, and hot embossing of biodegradable polymers. The developed plug filter is 500 μm long with base and apex plane diameters of 500 and 300 μm, respectively, and incorporates a laser-drilled through-hole with 44-μm effective diameter in the center. View full abstract»

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  • Electrodiffusion of Molecules in Aqueous Media: A Robust, Discretized Description for Electroporation and Other Transport Phenomena

    Page(s): 1514 - 1522
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (474 KB) |  | HTML iconHTML  

    Electrically driven transport of molecules and ions within aqueous electrolytes is of long-standing interest, with direct relevance to applications that include the delivery/release of biologically active solutes to/from cells and tissues. Examples include iontophoretic and electroporation-mediated drug delivery. Here, we describe a robust method for characterizing electrodiffusive transport in physiologic aqueous media. Specifically, we treat the case of solute present in sufficiently low concentration as to negligibly contribute to the total ionic current within the system. In this limiting case, which applies to many systems of interest, the predominant electrical behavior due to small ions is decoupled from solute transport. Thus, electrical behavior may be characterized using existing methods and treated as known in characterizing electrodiffusive molecular transport. First, we present traditional continuum equations governing electrodiffusion of charged solutes within aqueous electrolytes and then adapt them to discretized systems. Second, we examine the time-dependent and steady-state interfacial concentration gradients that result from the combination of diffusion and electrical drift. Third, we show how interfacial concentration gradients are related to electric field strength and duration. Finally, we examine how discretization size affects the accuracy of these methods. Overall these methods are motivated by and well suited to addressing an outstanding goal: estimation of the net ionic and molecular transport facilitated by electroporation in biological systems. View full abstract»

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  • Depth Discontinuity-Based Cup Segmentation From Multiview Color Retinal Images

    Page(s): 1523 - 1531
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (959 KB) |  | HTML iconHTML  

    Accurate segmentation of the cup region from retinal images is needed to derive relevant measurements for glaucoma assessment. A novel, depth discontinuity (in the retinal surface)-based approach to estimate the cup boundary is proposed in this paper. The proposed approach shifts focus from the cup region used by existing approaches to cup boundary. The given set of images, acquired sequentially, are related via a relative motion model and the depth discontinuity at the cup boundary is determined from cues such as motion boundary and partial occlusion. The information encoded by these cues is used to approximate the cup boundary with a set of best-fitting circles. The final boundary is found by considering points on these circles at different sectors using a confidence measure. Four different kinds of data sets ranging from synthetic to real image pairs, covering different multiview scenarios, have been used to evaluate the proposed method. The proposed method was found to yield an error reduction of 16% for cup-to-disk vertical diameter ratio (CDR) and 13% for cup-to-disk area ratio (CAR) estimation, over an existing monocular image-based cup segmentation method. The error reduction increased to 33% in CDR and 18% in CAR with the addition of a third view (image) which indicates the potential of the proposed approach. View full abstract»

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  • Phantom Model of Physiologic Intracranial Pressure and Cerebrospinal Fluid Dynamics

    Page(s): 1532 - 1538
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (666 KB) |  | HTML iconHTML  

    We describe herein a novel life-size phantom model of the intracranial cavity and its validation. The cerebrospinal fluid (CSF) domains including ventricular, cysternal, and subarachnoid spaces were derived via magnetic resonance imaging. Brain mechanical properties and cranio-spinal compliance were set based on published data. Both bulk and pulsatile physiologic CSF flow were modeled. Model validation was carried out by comparisons of flow and pressure measurements in the phantom with published in vivo data of healthy subjects. Physiologic intracranial pressure with 10 mmHg mean and 0.4 mmHg peak pulse amplitude was recorded in the ventricles. Peak CSF flow rates of 0.2 and 2 ml/s were measured in the cerebral aqueduct and subarachnoid space, respectively. The phantom constitutes a first-of-its-kind approach to modeling physiologic intracranial dynamics in vitro. Herein, we describe the phantom design and manufacturing, definition and implementation of its operating parameters, as well as the validation of the modeled dynamics. View full abstract»

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  • Embedding Topic Discovery in Conditional Random Fields Model for Segmenting Nuclei Using Multispectral Data

    Page(s): 1539 - 1549
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (926 KB) |  | HTML iconHTML  

    Segmentation of cells/nuclei is a challenging problem in 2-D histological and cytological images. Although a large number of algorithms have been proposed, newer efforts continue to be devoted to investigate robust models that could have high level of adaptability with regard to considerable amount of image variability. In this paper, we propose a multiclassification conditional random fields (CRFs) model using a combination of low-level cues (bottom-up) and high-level contextual information (top-down) for separating nuclei from the background. In our approach, the contextual information is extracted by an unsupervised topic discovery process, which efficiently helps to suppress segmentation errors caused by intensity inhomogeneity and variable chromatin texture. In addition, we propose a multilayer CRF, an extension of the traditional single-layer CRF, to handle high-dimensional dataset obtained through spectral microscopy, which provides combined benefits of spectroscopy and imaging microscopy, resulting in the ability to acquire spectral images of microscopic specimen. The approach is evaluated with color images, as well as spectral images. The overall accuracy of the proposed segmentation algorithm reaches 95% when applying multilayer CRF model to the spectral microscopy dataset. Experiments also show that our method outperforms seeded watershed, a widely used algorithm for cell segmentation. View full abstract»

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  • Neural Network Incorporating Meal Information Improves Accuracy of Short-Time Prediction of Glucose Concentration

    Page(s): 1550 - 1560
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (975 KB) |  | HTML iconHTML  

    Diabetes mellitus is one of the most common chronic diseases, and a clinically important task in its management is the prevention of hypo/hyperglycemic events. This can be achieved by exploiting continuous glucose monitoring (CGM) devices and suitable short-term prediction algorithms able to infer future glycemia in real time. In the literature, several methods for short-time glucose prediction have been proposed, most of which do not exploit information on meals, and use past CGM readings only. In this paper, we propose an algorithm for short-time glucose prediction using past CGM sensor readings and information on carbohydrate intake. The predictor combines a neural network (NN) model and a first-order polynomial extrapolation algorithm, used in parallel to describe, respectively, the nonlinear and the linear components of glucose dynamics. Information on the glucose rate of appearance after a meal is described by a previously published physiological model. The method is assessed on 20 simulated datasets and on 9 real Abbott FreeStyle Navigator datasets, and its performance is successfully compared with that of a recently proposed NN glucose predictor. Results suggest that exploiting meal information improves the accuracy of short-time glucose prediction. View full abstract»

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  • A State-Space Modeling Approach for Localization of Focal Current Sources From MEG

    Page(s): 1561 - 1571
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (686 KB) |  | HTML iconHTML  

    State-space modeling is a promising approach for current source reconstruction from magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of inverse solutions in a flexible manner. However, state-space model-based source localization research remains underdeveloped; extraction of spatially focal current sources and handling of the high dimensionality of the distributed source model remain problematic. In this study, we propose a novel state-space model-based method that resolves these problems, extending our previous source localization method to include a temporal constraint by state-space modeling. To enable focal current reconstruction, we account for spatially inhomogeneous temporal dynamics by introducing dynamics model parameters that differ for each cortical position. The model parameters and the intensity of the current sources are jointly estimated according to a Bayesian framework. We circumvent the high dimensionality of the problem by assuming prior distributions of the model parameters to reduce the sensitivity to unmodeled components, and by adopting variational Bayesian inference to reduce the computational cost. Through simulation experiments and application to real MEG data, we have confirmed that our proposed method successfully reconstructs focal current activities, which evolve with their temporal dynamics. View full abstract»

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  • Extrapolatable Analytical Functions for Tendon Excursions and Moment Arms From Sparse Datasets

    Page(s): 1572 - 1582
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (948 KB) |  | HTML iconHTML  

    Computationally efficient modeling of complex neuromuscular systems for dynamics and control simulations often requires accurate analytical expressions for moment arms over the entire range of motion. Conventionally, polynomial expressions are regressed from experimental data. But these polynomial regressions can fail to extrapolate, may require large datasets to train, are not robust to noise, and often have numerous free parameters. We present a novel method that simultaneously estimates both the form and parameter values of arbitrary analytical expressions for tendon excursions and moment arms over the entire range of motion from sparse datasets. This symbolic regression method based on genetic programming has been shown to find the appropriate form of mathematical expressions that capture the physics of mechanical systems. We demonstrate this method by applying it to 1) experimental data from a physical tendon-driven robotic system with arbitrarily routed multiarticular tendons and 2) synthetic data from musculoskeletal models. We show it outperforms polynomial regressions in the amount of training data, ability to extrapolate, robustness to noise, and representation containing fewer parameters-all critical to realistic and efficient computational modeling of complex musculoskeletal systems. View full abstract»

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  • Modeling the Field Distribution in Deep Brain Stimulation: The Influence of Anisotropy of Brain Tissue

    Page(s): 1583 - 1592
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1037 KB) |  | HTML iconHTML  

    The neurosurgical method of deep brain stimulation (DBS) is used to treat symptoms of movement disorders like Parkinson's disease by implanting stimulation electrodes in deep brain areas. The aim of this study was to examine the field distribution in DBS and the role of heterogeneous and anisotropic material properties in the brain areas where stimulation is applied. Finite element models of the human brain were developed comprising tissue heterogeneity and anisotropy. The tissue data were derived from averaged magnetic resonance imaging and diffusion tensor imaging datasets. Unilateral stimulation of the subthalamic nucleus (STN) was computed using an accurate model of an electrode used in clinical treatment of DBS extended with an encapsulation layer around the electrode body. Computations of anisotropic and isotropic brain models, which consider resistive tissue properties for unipolar and bipolar electrode configurations, were carried out. Electrode position was varied within an area around the stimulation center. Results have shown a deviation of 2 % between anisotropic and isotropic field distributions in the vicinity of the STN. The sensitivity of this deviation referring to the electrode position remained small, but increased when the electrode position approached areas of high anisotropy. View full abstract»

    Open Access
  • A Multiscale Model for Bioimpedance Dispersion of Liver Tissue

    Page(s): 1593 - 1597
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (334 KB) |  | HTML iconHTML  

    Radio-frequency ablation (RFA) has been used in liver surgery to minimize blood loss during tissue division. However, the current RFA tissue division method lacks an effective way of determining the stoppage of blood flow. There is limitation on the current state-of-the-art laser Doppler flow sensor due to its small sensing area. A new technique was proposed to use bioimpedance for blood flow sensing. This paper discusses a new geometrical multiscale model of the liver bioimpedance incorporating blood flow impedance. This model establishes correlation between the physical tissue structure and bioimpedance measurement. The basic Debye structure within a multilevel framework is used in the model to account for bioimpedance dispersion. This dispersion is often explained by the Cole-Cole model that includes a constant phase element without physical explanation. Our model is able to account for reduced blood flow in its output with changes in permittivity in gamma dispersion that is mainly due to the polarization of water molecules. This study demonstrates the potential of a multiscale model in determining the stoppage of blood flow during surgery. View full abstract»

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  • Filter-Based Methodology for the Location of Hot Spots in Proteins and Exons in DNA

    Page(s): 1598 - 1609
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1233 KB) |  | HTML iconHTML  

    The so-called receiver operating characteristic technique is used as a tool in an optimization procedure for the improvement and assessment of a filter-based methodology for the location of hot spots in protein sequences and exons in DNA sequences. By optimizing the characteristic values of the nucleotides, high efficiency as well as improved accuracy can be achieved relative to results obtained with the electron-ion interaction potentials. On the other hand, by using the proposed filter-based methodology with binary sequences, improved accuracy can be achieved although the efficiency is somewhat compromised relative to that achieved using the optimized characteristic values. Extensive experimental results, evaluated using measures such as the g-mean, the Matthews correlation coefficient, and the chi-square statistic, show that the filter-based methodology performs much better than existing techniques using the short-time discrete Fourier transform, particularly in applications where short exons are involved. View full abstract»

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  • Classification of Periodic Activities Using the Wasserstein Distance

    Page(s): 1610 - 1619
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (691 KB) |  | HTML iconHTML  

    In this paper, we introduce a novel nonparametric classification technique based on the use of the Wasserstein distance. The proposed scheme is applied in a biomedical context for the analysis of recorded accelerometer data: the aim is to retrieve three types of periodic activities (walking, biking, and running) from a time-frequency representation of the data. The main interest of the use of the Wasserstein distance lies in the fact that it is less sensitive to the location of the frequency peaks than to the global structure of the frequency pattern, allowing us to detect activities almost independently of their speed or incline. Our system is tested on a 24-subject corpus: results show that the use of Wasserstein distance combined with some supervised learning techniques allows us to compare with some more complex classification systems. View full abstract»

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  • Automatic Detection and Quantification of Tree-in-Bud (TIB) Opacities From CT Scans

    Page(s): 1620 - 1632
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (989 KB) |  | HTML iconHTML  

    This study presents a novel computer-assisted detection (CAD) system for automatically detecting and precisely quantifying abnormal nodular branching opacities in chest computed tomography (CT), termed tree-in-bud (TIB) opacities by radiology literature. The developed CAD system in this study is based on 1) fast localization of candidate imaging patterns using local scale information of the images, and 2) Möbius invariant feature extraction method based on learned local shape and texture properties of TIB patterns. For fast localization of candidate imaging patterns, we use ball-scale filtering and, based on the observation of the pattern of interest, a suitable scale selection is used to retain only small size patterns. Once candidate abnormality patterns are identified, we extract proposed shape features from regions where at least one candidate pattern occupies. The comparative evaluation of the proposed method with commonly used CAD methods is presented with a dataset of 60 chest CTs (laboratory confirmed 39 viral bronchiolitis human parainfluenza CTs and 21 normal chest CTs). The quantitative results are presented as the area under the receiver operator characteristics curves and a computer score (volume affected by TIB) provided as an output of the CAD system. In addition, a visual grading scheme is applied to the patient data by three well-trained radiologists. Interobserver and observer-computer agreements are obtained by the relevant statistical methods over different lung zones. Experimental results demonstrate that the proposed CAD system can achieve high detection rates with an overall accuracy of 90.96%. Moreover, correlations of observer-observer (R2=0.8848, p <; 0.01) and observer-CAD agreements (R2=0.824, p <; 0.01) validate the feasibility of the use of the proposed CAD system in detecting and quantifying TIB patterns. View full abstract»

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  • Nonstationary Harmonic Modeling for ECG Removal in Surface EMG Signals

    Page(s): 1633 - 1640
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (724 KB) |  | HTML iconHTML  

    We present a compact approach for mitigating the presence of electrocardiograms (ECG) in surface electromyographic (EMG) signals by means of time-variant harmonic modeling of the cardiac artifact. Heart rate and QRS complex variability, which often account for amplitude and frequency time variations of the ECG, are simultaneously captured by a set of third-order constant-coefficient polynomials modulating a stationary harmonic basis in the analysis window. Such a characterization allows us to significantly suppress ECG from the mixture by preserving most of the EMG signal content at low frequencies (less than 20 Hz). Moreover, the resulting model is linear in parameters and the least-squares solution to the corresponding linear system of equations efficiently provides model parameter estimates. The comparative results suggest that the proposed method outperforms two reference methods in terms of the EMG preservation at low frequencies. View full abstract»

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  • Time-Based Compression and Classification of Heartbeats

    Page(s): 1641 - 1648
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (565 KB) |  | HTML iconHTML  

    Heart function measured by electrocardiograms (ECG) is crucial for patient care. ECG generated waveforms are used to find patterns of irregularities in cardiac cycles in patients. In many cases, irregularities evolve over an extended period of time that requires continuous monitoring. However, this requires wireless ECG recording devices. These devices consist of an enclosed system that includes electrodes, processing circuitry, and a wireless communication block imposing constraints on area, power, bandwidth, and resolution. In order to provide continuous monitoring of cardiac functions for real-time diagnostics, we propose a methodology that combines compression and analysis of heartbeats. The signal encoding scheme is the time-based integrate and fire sampler. The diagnostics can be performed directly on the samples avoiding reconstruction required by the competing finite rate of innovation and compressed sensing. As an added benefit, our scheme provides an efficient hardware implementation and a compressed representation for the ECG recordings, while still preserving discriminative features. We demonstrate the performance of our approach through a heartbeat classification application consisting of normal and irregular heartbeats known as arrhythmia. Our approach that uses simple features extracted from ECG signals is comparable to results in the published literature. View full abstract»

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  • High-Density Myoelectric Pattern Recognition Toward Improved Stroke Rehabilitation

    Page(s): 1649 - 1657
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (836 KB) |  | HTML iconHTML  

    Myoelectric pattern-recognition techniques have been developed to infer user's intention of performing different functional movements. Thus electromyogram (EMG) can be used as control signals of assisted devices for people with disabilities. Pattern-recognition-based myoelectric control systems have rarely been designed for stroke survivors. Aiming at developing such a system for improved stroke rehabilitation, this study assessed detection of the affected limb's movement intention using high-density surface EMG recording and pattern-recognition techniques. Surface EMG signals comprised of 89 channels were recorded from 12 hemiparetic stroke subjects while they tried to perform 20 different arm, hand, and finger/thumb movements involving the affected limb. A series of pattern-recognition algorithms were implemented to identify the intended tasks of each stroke subject. High classification accuracies (96.1% ± 4.3%) were achieved, indicating that substantial motor control information can be extracted from paretic muscles of stroke survivors. Such information may potentially facilitate improved stroke rehabilitation. View full abstract»

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  • Enhancing the Accuracy of Subcutaneous Glucose Sensors: A Real-Time Deconvolution-Based Approach

    Page(s): 1658 - 1669
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1066 KB) |  | HTML iconHTML  

    Minimally invasive continuous glucose monitoring (CGM) sensors can greatly help diabetes management. Most of these sensors consist of a needle electrode, placed in the subcutaneous tissue, which measures an electrical current exploiting the glucose-oxidase principle. This current is then transformed to glucose levels after calibrating the sensor on the basis of one, or more, self-monitoring blood glucose (SMBG) samples. In this study, we design and test a real-time signal-enhancement module that, cascaded to the CGM device, improves the quality of its output by a proper postprocessing of the CGM signal. In fact, CGM sensors measure glucose in the interstitium rather than in the blood compartment. We show that this distortion can be compensated by means of a regularized deconvolution procedure relying on a linear regression model that can be updated whenever a pair of suitably sampled SMBG references is collected. Tests performed both on simulated and real data demonstrate a significant accuracy improvement of the CGM signal. Simulation studies also demonstrate the robustness of the method against departures from nominal conditions, such as temporal misplacement of the SMBG samples and uncertainty in the blood-to-interstitium glucose kinetic model. Thanks to its online capabilities, the proposed signal-enhancement algorithm can be used to improve the performance of CGM-based real-time systems such as the hypo/hyper glycemic alert generators or the artificial pancreas. View full abstract»

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  • Surface Mosaics of the Bladder Reconstructed From Endoscopic Video for Automated Surveillance

    Page(s): 1670 - 1680
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (830 KB) |  | HTML iconHTML  

    Flexible cystoscopy is frequently performed for recurrent bladder cancer surveillance, making it the most expensive cancer to treat over the patient's lifetime. An automated bladder surveillance system is being developed to robotically scan the bladder surface using an ultrathin and highly flexible endoscope. Such a system would allow cystoscopic procedures to be overseen by technical staff while urologists could review cystoscopic video postoperatively. In this paper, we demonstrate a method for reconstructing the surface of the whole bladder from endoscopic video using structure from motion. Video is acquired from a custom ultrathin and highly flexible endoscope that can retroflex to image the entire internal surface of the bladder. Selected frames are subsequently stitched into a mosaic and mapped to a reconstructed surface, creating a 3-D surface model of the bladder that can be expediently reviewed. Our software was tested on endoscopic video of an excised pig bladder. The resulting reconstruction possessed a projection error of 1.66 pixels on average and covered 99.6% of the bladder surface area. View full abstract»

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  • Multilevel Segmentation of Histopathological Images Using Cooccurrence of Tissue Objects

    Page(s): 1681 - 1690
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (939 KB) |  | HTML iconHTML  

    This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship. As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images. Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function. This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the final result. The experiments on 200 colon tissue images reveal that the proposed approach-the object cooccurrence features together with the multilevel segmentation algorithm-is effective to obtain high-quality results. The experiments also show that it improves the segmentation results compared to the previous approaches. View full abstract»

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  • Real-Time Automatic Tuning of Noise Suppression Algorithms for Cochlear Implant Applications

    Page(s): 1691 - 1700
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1366 KB) |  | HTML iconHTML  

    The performance of cochlear implants deteriorates in noisy environments compared to quiet conditions. This paper presents an adaptive cochlear implant system, which is capable of classifying the background noise environment in real time for the purpose of adjusting or tuning its noise suppression algorithm to that environment. The tuning is done automatically with no user intervention. Five objective quality measures are used to show the superiority of this adaptive system compared to a conventional fixed noise-suppression system. Steps taken to achieve the real-time implementation of the entire system, incorporating both the cochlear implant speech processing and the background noise suppression, on a portable PDA research platform are presented along with the timing results. View full abstract»

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  • Generating Stochastic Gene Regulatory Networks Consistent With Pathway Information and Steady-State Behavior

    Page(s): 1701 - 1710
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (979 KB) |  | HTML iconHTML  

    We present a procedure to generate a stochastic genetic regulatory network model consistent with pathway information. Using the stochastic dynamics of Markov chains, we produce a model constrained by the prior knowledge despite the sometimes incomplete, time independent, and often conflicting nature of these pathways. We apply the Markov theory to study the model's long run behavior and introduce a biologically important transformation to aid in comparison with real biological outcome prediction in the steady-state domain. Our technique produces biologically faithful models without the need for rate kinetics, detailed timing information, or complex inference procedures. To demonstrate the method, we produce a model using 28 pathways from the biological literature pertaining to the transcription factor family nuclear factor-κB. Predictions from this model in the steady-state domain are then validated against nine mice knockout experiments. View full abstract»

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Aims & Scope

IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.

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Meet Our Editors

Editor-in-Chief
Bin He
Department of Biomedical Engineering