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Biomedical Engineering, IEEE Transactions on

Issue 5 • Date May 2012

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  • [Front cover]

    Publication Year: 2012 , Page(s): C1
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  • IEEE Transactions on Biomedical Engineering publication information

    Publication Year: 2012 , Page(s): C2
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  • Table of contents

    Publication Year: 2012 , Page(s): 1197 - 1198
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  • Deep Tissue Photoacoustic Imaging Using a Miniaturized 2-D Capacitive Micromachined Ultrasonic Transducer Array

    Publication Year: 2012 , Page(s): 1199 - 1204
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (399 KB) |  | HTML iconHTML  

    In this paper, we demonstrate 3-D photoacoustic imaging (PAI) of light absorbing objects embedded as deep as 5 cm inside strong optically scattering phantoms using a miniaturized (4 mm × 4 mm × 500 μm), 2-D capacitive micromachined ultrasonic transducer (CMUT) array of 16 × 16 elements with a center frequency of 5.5 MHz. Two-dimensional tomographic images and 3-D volumetric images of the objects placed at different depths are presented. In addition, we studied the sensitivity of CMUT-based PAI to the concentration of indocyanine green dye at 5 cm depth inside the phantom. Under optimized experimental conditions, the objects at 5 cm depth can be imaged with SNR of about 35 dB and a spatial resolution of approximately 500 μm. Results demonstrate that CMUTs with integrated front-end amplifier circuits are an attractive choice for achieving relatively high depth sensitivity for PAI. View full abstract»

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  • A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies

    Publication Year: 2012 , Page(s): 1205 - 1218
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1091 KB) |  | HTML iconHTML  

    Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP presence and extent via a microscope, a time-consuming and subjective process. With the advent of digital pathology, computer-aided algorithms can now be applied to disease detection on digitized glass slides. The size of these digitized histology images (hundreds of millions of pixels) presents a formidable challenge for any computerized image analysis program. In this paper, we present a boosted Bayesian multiresolution (BBMR) system to identify regions of CaP on digital biopsy slides. Such a system would serve as an important preceding step to a Gleason grading algorithm, where the objective would be to score the invasiveness and severity of the disease. In the first step, our algorithm decomposes the whole-slide image into an image pyramid comprising multiple resolution levels. Regions identified as cancer via a Bayesian classifier at lower resolution levels are subsequently examined in greater detail at higher resolution levels, thereby allowing for rapid and efficient analysis of large images. At each resolution level, ten image features are chosen from a pool of over 900 first-order statistical, second-order co-occurrence, and Gabor filter features using an AdaBoost ensemble method. The BBMR scheme, operating on 100 images obtained from 58 patients, yielded: 1) areas under the receiver operating characteristic curve (AUC) of 0.84, 0.83, and 0.76, respectively, at the lowest, intermediate, and highest resolution levels and 2) an eightfold savings in terms of computational time compared to running the algorithm directly at full (highest) resolution. The BBMR model outperformed (in terms of AUC): 1) individual features (no ensemble) and 2) a random forest classifier ensemble obtained by bagging multiple decision tree classifiers. The apparent drop-off in AUC at higher image resolutions is due to lack of fine detail in the expert annotation of CaP and is not an artifact of the- classifier. The implicit feature selection done via the AdaBoost component of the BBMR classifier reveals that different classes and types of image features become more relevant for discriminating between CaP and benign areas at different image resolutions. View full abstract»

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  • Efficient Computational Methods for Strongly Coupled Cardiac Electromechanics

    Publication Year: 2012 , Page(s): 1219 - 1228
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (631 KB) |  | HTML iconHTML  

    Strongly coupled cardiac electromechanical models can further our understanding of the relative importance of feedback mechanisms in the heart, but computational challenges currently remain a major obstacle, which limit their widespread use. To address this issue, we present a set of efficient computational methods including an efficient adaptive cell model integration scheme and a solution method for the monodomain equations that maintains high conduction velocity for time steps greater than 0.1 ms. We also present a novel method for increasing the efficiency of simulating electromechanical coupling, which shows a significant reduction in computational cost of the mechanical component on a personalized left ventricular geometry with an active contraction cell model reparametrized for human cells. View full abstract»

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  • EIT Forward Problem Parallel Simulation Environment with Anisotropic Tissue and Realistic Electrode Models

    Publication Year: 2012 , Page(s): 1229 - 1239
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1063 KB) |  | HTML iconHTML  

    Electrical impedance tomography (EIT) is an imaging technology based on impedance measurements. To retrieve meaningful insights from these measurements, EIT relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of current flows therein. The nonhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeoff between physical accuracy and technical feasibility, which at present severely limits the capabilities of EIT. This work presents a complete algorithmic flow for an accurate EIT modeling environment featuring high anatomical fidelity with a spatial resolution equal to that provided by an MRI and a novel realistic complete electrode model implementation. At the same time, we demonstrate that current graphics processing unit (GPU)-based platforms provide enough computational power that a domain discretized with five million voxels can be numerically modeled in about 30 s. View full abstract»

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  • High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts

    Publication Year: 2012 , Page(s): 1240 - 1252
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (941 KB) |  | HTML iconHTML  

    We present a system for accurately quantifying the presence and extent of stain on account of a vascular biomarker on tissue microarrays. We demonstrate our flexible, robust, accurate, and high-throughput minimally supervised segmentation algorithm, termed hierarchical normalized cuts (HNCuts) for the specific problem of quantifying extent of vascular staining on ovarian cancer tissue microarrays. The high-throughput aspect of HNCut is driven by the use of a hierarchically represented data structure that allows us to merge two powerful image segmentation algorithms-a frequency weighted mean shift and the normalized cuts algorithm. HNCuts rapidly traverses a hierarchical pyramid, generated from the input image at various color resolutions, enabling the rapid analysis of large images (e.g., a 1500 × 1500 sized image under 6 s on a standard 2.8-GHz desktop PC). HNCut is easily generalizable to other problem domains and only requires specification of a few representative pixels (swatch) from the object of interest in order to segment the target class. Across ten runs, the HNCut algorithm was found to have average true positive, false positive, and false negative rates (on a per pixel basis) of 82%, 34%, and 18%, in terms of overlap, when evaluated with respect to a pathologist annotated ground truth of the target region of interest. By comparison, a popular supervised classifier (probabilistic boosting trees) was only able to marginally improve on the true positive and false negative rates (84% and 14%) at the expense of a higher false positive rate (73%), with an additional computation time of 62% compared to HNCut. We also compared our scheme against a k-means clustering approach, which both the HNCut and PBT schemes were able to outperform. Our success in accurately quantifying the extent of vascular stain on ovarian cancer TMAs suggests that HNCut could be a very powerful tool in digital pathology and bioinformatics applications where it could be used t- facilitate computer-assisted prognostic predictions of disease outcome. View full abstract»

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  • Three-Dimensional Motor Neuron Morphology Estimation in the Drosophila Ventral Nerve Cord

    Publication Year: 2012 , Page(s): 1253 - 1263
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1075 KB) |  | HTML iconHTML  

    Type-specific dendritic arborization patterns dictate synaptic connectivity and are fundamental determinants of neuronal function. We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse neuronal morphologies of individual motor neurons (MNs) and understand underlying principles of synaptic connectivity in a motor circuit. Our computational approach aims at the reconstruction of the neuron morphology, namely the robust segmentation of the neuron volumes from their surroundings with the simultaneous partitioning into their compartments, namely the soma, axon, and dendrites. We use the idea of cosegmentation, where every image along the z -axis (depth) is segmented using information from “neighboring” depths. We use 3-D Haar-like features to model appearance. Because soma and axon are determined by their distinctive shapes, we define an implicit shape representation of the 2-D segmentation sets to drive cosegmentation and achieve the desired partitioning. We validate our method using image stacks depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy. View full abstract»

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  • Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease

    Publication Year: 2012 , Page(s): 1264 - 1271
    Cited by:  Papers (13)
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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (390 KB) |  | HTML iconHTML  

    There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD. View full abstract»

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  • Anisotropic Processing of Laser Speckle Images Improves Spatiotemporal Resolution

    Publication Year: 2012 , Page(s): 1272 - 1280
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1176 KB) |  | HTML iconHTML  

    Laser speckle contrast imaging (LSCI) is a full field optical imaging technique, capable of imaging blood flow without the introduction of any exogenous dyes. Spatial and temporal resolution in LSCI images depend on how pixels are chosen from the raw image stack for contrast processing. However, all processing schemes are based on isotropic treatment of the spatial neighborhood about each pixel, restricting further improvement in spatiotemporal resolution and image quality. We present a novel spatiotemporal processing scheme for LSCI where the spatial neighborhood is anisotropic, that is, restricted along a specific direction that matches direction of blood flow. The technique allows for a significant increase in temporal resolution, from conventionally used 40 or 80 frames to just three frames; while simultaneously achieving 23% and 47% higher signal-to-noise ratios over concurrent spatiotemporal schemes, when imaging rapid and slow functional changes in blood flow, respectively. We present the concept, justification, and performance evaluation of the novel scheme and demonstrate its suitability for imaging rapid changes in blood flow. Anisotropic LSCI was able to monitor the heart rate associated fluctuations in intravascular blood flow and showed them to be as high as 28% of the mean. View full abstract»

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  • A Glucose-Specific Metric to Assess Predictors and Identify Models

    Publication Year: 2012 , Page(s): 1281 - 1290
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (822 KB) |  | HTML iconHTML  

    In diabetes, the mean square error (MSE) metric is extensively used for assessing glucose prediction methods and identifying glucose models. One limitation of this metric is that, by equally treating errors in hypo-, eu-, and hyperglycemia, it is not able to weight the different clinical impact of errors in these three situations. In this paper, we propose a new cost function, which overcomes this limitation and can be used in place of MSE for several scopes, in particular for assessing the quality of glucose predictors and identifying glucose models. The new metric called glucose-specific MSE (gMSE) modifies MSE with a Clark error grid inspired penalty function, which penalizes overestimation in hypoglycemia and underestimation in hyperglycemia, i.e., the most harmful conditions on a clinical perspective. From a mathematical point of view, gMSE retains sensitivity of MSE and inherits some of its important mathematical features, in particular it has no local minima, simplifying the optimization. This makes it suitable for model identification purposes also. First, the goodness of it is demonstrated by means of three experiments, designed ad hoc to evidence its sensitivity to accuracy, precision, and distortion in glucose predictions. Second, a prediction assessment problem is presented, in which two real prediction profiles are compared. Results show that the MSE chooses the worst clinical situation, while gMSE correctly selects the situation with less clinical risk. Finally, we also demonstrate that models identified minimizing gMSE are more accurate in potentially harmful situations (hypo- and hyperglycemia) than those obtained by MSE. View full abstract»

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  • A Microphone Array System for Automatic Fall Detection

    Publication Year: 2012 , Page(s): 1291 - 1301
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1141 KB) |  | HTML iconHTML  

    More than a third of elderly fall each year in the United States. It has been shown that the longer the lie on the floor, the poorer is the outcome of the medical intervention. To reduce delay of the medical intervention, we have developed an acoustic fall detection system (acoustic-FADE) that automatically detects a fall and reports it promptly to the caregiver. Acoustic-FADE consists of a circular microphone array that captures the sounds in a room. When a sound is detected, acoustic-FADE locates the source, enhances the signal, and classifies it as “fall” or “nonfall.” The sound source is located using the steered response power with phase transform technique, which has been shown to be robust under noisy environments and resilient to reverberation effects. Signal enhancement is performed by the beamforming technique based on the estimated sound source location. Height information is used to increase the specificity. The mel-frequency cepstral coefficient features computed from the enhanced signal are utilized in the classification process. We have evaluated the performance of acoustic-FADE using simulated fall and nonfall sounds performed by three stunt actors trained to behave like elderly under different environmental conditions. Using a dataset consisting of 120 falls and 120 nonfalls, the acoustic-FADE achieves 100% sensitivity at a specificity of 97%. View full abstract»

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  • Analyzing Functional Brain Connectivity by Means of Commute Times: A New Approach and its Application to Track Event-Related Dynamics

    Publication Year: 2012 , Page(s): 1302 - 1309
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (550 KB) |  | HTML iconHTML  

    There is growing interest in studying the association of functional connectivity patterns with particular cognitive tasks. The ability of graphs to encapsulate relational data has been exploited in many related studies, where functional networks (sketched by different neural synchrony estimators) are characterized by a rich repertoire of graph-related metrics. We introduce commute times (CTs) as an alternative way to capture the true interplay between the nodes of a functional connectivity graph (FCG). CT is a measure of the time taken for a random walk to setout and return between a pair of nodes on a graph. Its computation is considered here as a robust and accurate integration, over the FCG, of the individual pairwise measurements of functional coupling. To demonstrate the benefits from our approach, we attempted the characterization of time evolving connectivity patterns derived from EEG signals recorded while the subject was engaged in an eye-movement task. With respect to standard ways, which are currently employed to characterize connectivity, an improved detection of event-related dynamical changes is noticeable. CTs appear to be a promising technique for deriving temporal fingerprints of the brain's dynamic functional organization. View full abstract»

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  • On the Mechanics of Functional Asymmetry in Bipedal Walking

    Publication Year: 2012 , Page(s): 1310 - 1318
    Cited by:  Papers (7)
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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1125 KB)  

    This paper uses two symmetrical models, the passive compass-gait biped and a five-link 3-D biped, to computationally investigate the cause and function of gait asymmetry. We show that for a range of slope angles during passive 2-D walking and mass distributions during controlled 3-D walking, these models have asymmetric walking patterns between the left and right legs due to the phenomenon of spontaneous symmetry-breaking. In both cases a stable asymmetric family of gaits emerges from a symmetric family of gaits as the total energy increases (e.g., fast speeds). The ground reaction forces of each leg reflect different roles, roughly corresponding to support, propulsion, and motion control as proposed by the hypothesis of functional asymmetry in able-bodied human walking. These results suggest that body mechanics, independent of neurophysiological mechanisms such as leg dominance, may contribute to able-bodied gait asymmetry. View full abstract»

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  • Propagation Pattern Analysis During Atrial Fibrillation Based on Sparse Modeling

    Publication Year: 2012 , Page(s): 1319 - 1328
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1388 KB) |  | HTML iconHTML  

    In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using simulated data, both optimization methods were superior to LS estimation with respect to detection and estimation performance. The normalized error between the true and estimated model parameters dropped from 0.20± 0.04 for LS estimation to 0.03 ± 0.01 for both aLASSO and dLASSO when the number of available data samples exceeded the number of model parameters by a factor of 5. For shorter data segments, the error reduction was more pronounced and information on the distance gained in importance. Propagation pattern analysis was also studied on intracardiac AF data, the results showing that the identification of propagation patterns is substantially simplified by the sparsity assumption. View full abstract»

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  • Projection Versus Prewhitening for EEG Interference Suppression

    Publication Year: 2012 , Page(s): 1329 - 1338
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (836 KB) |  | HTML iconHTML  

    Suppression of strong, spatially correlated background interference is a challenge associated with electroencephalography (EEG) source localization problems. The most common way of dealing with such interference is through the use of a prewhitening transformation based on an estimate of the covariance of the interference plus noise. This approach is based on strong assumptions regarding temporal stationarity of the data, which do not commonly hold in EEG applications. In addition, prewhitening cannot typically be implemented directly due to ill conditioning of the covariance matrix, and ad hoc regularization is often necessary. Using both simulation examples and experiments involving real EEG data with auditory evoked responses, we demonstrate that a straightforward interference projection method is significantly more robust than prewhitening for EEG source localization. View full abstract»

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  • Efficient Dipole Parameter Estimation in EEG Systems With Near-ML Performance

    Publication Year: 2012 , Page(s): 1339 - 1348
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (641 KB) |  | HTML iconHTML  

    Source signals that have strong temporal correlation can pose a challenge for high-resolution EEG source localization algorithms. In this paper, we present two methods that are able to accurately locate highly correlated sources in situations where other high-resolution methods such as multiple signal classification and linearly constrained minimum variance beamforming fail. These methods are based on approximations to the optimal maximum likelihood (ML) approach, but offer significant computational advantages over ML when estimates of the equivalent EEG dipole orientation and moment are required in addition to the source location. The first method uses a two-stage approach in which localization is performed assuming an unstructured dipole moment model, and then the dipole orientation is obtained by using these estimates in a second step. The second method is based on the use of the noise subspace fitting concept, and has been shown to provide performance that is asymptotically equivalent to the direct ML method. Both techniques lead to a considerably simpler optimization than ML since the estimation of the source locations and dipole moments is decoupled. Examples using data from simulations and auditory experiments are presented to illustrate the performance of the algorithms. View full abstract»

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  • Recording of Electric Signal Passing Through a Pylon in Direct Skeletal Attachment of Leg Prostheses With Neuromuscular Control

    Publication Year: 2012 , Page(s): 1349 - 1353
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2909 KB) |  | HTML iconHTML  

    Direct recordings were made of electrical signals emanating from the muscles in a rabbit's residuum. The signals were transmitted via wires attached on one end to the muscles, and on the other to an external recording system. The cable was held in a titanium tube inside a pylon that had been transcutaneously implanted into the residuum's bone. The tube was surrounded by porous titanium cladding to enhance its bond with the bone and with the skin of the residuum. This study was the first known attempt to merge the technology of direct skeletal attachment of limb prostheses with the technology of neuromuscular control of prostheses, providing a safe and reliable passage of the electrical signal from the muscles inside the residuum to the outside recording system. View full abstract»

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  • Model-Based Reconstruction Integrated With Fluence Compensation for Photoacoustic Tomography

    Publication Year: 2012 , Page(s): 1354 - 1363
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1019 KB) |  | HTML iconHTML  

    Photoacoustic (PA) tomography (PAT) is a rapidly developing imaging modality that can provide high contrast and spatial-resolution images of light-absorption distribution in tissue. However, reconstruction of the absorption distribution is affected by nonuniform light fluence. This paper introduces a reconstruction method for reducing amplification of noise and artifacts in low-fluence regions. In this method, fluence compensation is integrated into model-based reconstruction, and the absorption distribution is iteratively updated. At each iteration, we calculate the residual between detected PA signals and the signals computed by a forward model using the initial pressure, which is the product of estimated voxel value and light fluence. By minimizing the residual, the reconstructed values converge to the true absorption distribution. In addition, we developed a matrix compression method for reducing memory requirements and accelerating reconstruction speed. The results of simulation and phantom experiments indicate that the proposed method provides a better contrast-to-noise ratio (CNR) in low-fluence regions. We expect that the capability of increasing imaging depth will broaden the clinical applications of PAT. View full abstract»

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  • Speech Understanding Performance of Cochlear Implant Subjects Using Time–Frequency Masking-Based Noise Reduction

    Publication Year: 2012 , Page(s): 1364 - 1373
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1054 KB) |  | HTML iconHTML  

    Cochlear implant (CI) recipients report severe degradation of speech understanding under noisy conditions. Most CI recipients typically can require about 10-25 dB higher signal-to-noise ratio than normal hearing (NH) listeners in order to achieve similar speech understanding performance. In recent years, significant emphasis has been put on binaural algorithms, which not only make use of the head shadow effect, but also have two or more microphone signals at their disposal to generate binaural inputs. Most of the CI recipients today are unilaterally implanted but they can still benefit from the binaural processing utilizing a contralateral microphone. The phase error filtering (PEF) algorithm tries to minimize the phase error variance utilizing a time-frequency mask for noise reduction. Potential improvement in speech intelligibility offered by the algorithm is evaluated with four different kinds of mask functions. The study reveals that the PEF algorithm which uses a contralateral microphone but unilateral presentation provides considerable improvement in intelligibility for both NH and CI subjects. Further, preference rating test suggests that CI subjects can tolerate higher levels of distortions than NH subjects, and therefore, more aggressive noise reduction for CI recipients is possible. View full abstract»

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  • Fractal Dimension as a Measure of Altered Actin Cytoskeleton in MC3T3-E1 Cells Under Simulated Microgravity Using 3-D/2-D Clinostats

    Publication Year: 2012 , Page(s): 1374 - 1380
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (581 KB) |  | HTML iconHTML  

    Osteoblasts, the bone-forming cells, respond to various mechanical forces, such as stretch and fluid shear force in essentially similar ways. The cytoskeleton, as the load-bearing architecture of the cell, is sensitive to altered inertial forces. Disruption of the cytoskeleton will result in alteration of cellular structure and function. However, it is difficult to quantitatively illustrate cytoskeletal rearrangement because of the complexity of cytoskeletal structure. Usually, the morphological changes in actin organization caused by external stimulus are basically descriptive. In this study, fractal dimensions (D) analysis was used to quantify the morphological changes in the actin cytoskeleton of osteoblast-like cells (MC3T3-E1) under simulated microgravity using 3-D/2-D clinostats. The ImageJ software was used to count the fractal dimension of actin cytoskeleton by box-counting methods. Real-time PCR and immunofluroscent assays were used to further confirm the results obtained by fractal dimension analysis. The results showed significant decreases in D value of actin cytoskeleton, β-actin mRNA expression, and the mean fluorescence intensity of F-actin in osteoblast-like cells after 24 or 48 h of incubation under 3-D/2-D clinorotation condition compared with control. The findings indicate that 3-D/2-D clinorotation affects both actin cytoskeleton architecture and mRNA expression, and fractal may be a promising approach for quantitative analysis of the changes in cytoskeleton in different environments. View full abstract»

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  • High-Frequency Electrical Stimulation of Cardiac Cells and Application to Artifact Reduction

    Publication Year: 2012 , Page(s): 1381 - 1390
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (806 KB) |  | HTML iconHTML  

    A novel modality for the electrical stimulation of cardiac cells is described. The technique is based on HF stimulation-burst of HF (1-25 kHz) biphasic square waves-to depolarize the cells and trigger action potentials (APs). HF stimulation was demonstrated in HL-1 cardiomyocyte cultures using microelectrode arrays, and the underlying mechanisms were investigated using single-cell model simulations. Current thresholds for HF stimulation increased at higher frequencies or shorter burst durations, and were typically higher than thresholds for single biphasic pulses. Nonetheless, owing to the decreasing impedance of metal electrodes with increasing frequencies, HF bursts resulted in reduced electrode voltages (up to four fold). Such lowered potentials might be beneficial in reducing the probability of irreversible electrochemical reactions and tissue damage, especially for long-term stimulation. More significantly, stimulation at frequencies higher than the upper limit of the AP power spectrum allows effective artifact reduction by low-pass filtering. Shaping of the burst envelope provides further reduction of the remaining artifact. This ability to decouple extracellular stimulation and recording in the frequency domain allowed detection of APs during stimulation-something previously not achievable to the best of our knowledge. View full abstract»

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  • In Vivo Validation of Longitudinal–Circumferential Area Change Ratio to Estimate Myofiber Shortening in the Heart

    Publication Year: 2012 , Page(s): 1391 - 1397
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (861 KB) |  | HTML iconHTML  

    The aim of this paper was to validate area change ratio (%AC) against myofiber shortening (%λf) in the heart in vivo. %AC is emerging as a mechanical index that may approximate %λf by incorporating both circumferential and longitudinal shortening. However, the physiological significance of %AC remains unclear. We studied the time course of %AC in the anterior midleft ventricular wall of normal canine heart in vivo (n = 14) during atrial pacing over the entire cardiac cycle using transmurally implanted markers and biplane cineradiography (8 ms/frame). %AC was calculated as the myocardial area change relative to the elemental material area on the circumferential-longitudinal plane at the reference configuration (=end diastole). %AC was compared with % λf that was determined from the transmural fiber orientation directly measured in the heart tissue. The time course of both %AC and %λf was determined in the subepicardial, midwall, and subendocardial layers. The time course of %AC and %λf was significantly different, and the difference was more pronounced towards the endocardium. %AC consistently overestimated %λf. The timing of the peak %AC was significantly delayed compared to that of the peak %λf. We conclude that %AC is significantly different from %λf both in magnitude and timing in vivo. %AC overestimates %λf, and the overestimation is worse toward the endocardial layers. This may be a potentially important limitation when applying %AC to optimization and responder identification for cardiac resynchronization therapy. View full abstract»

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  • Modulation of Spiral-Wave Dynamics and Spontaneous Activity in a Fibroblast/Myocyte Heterocellular Tissue–-A Computational Study

    Publication Year: 2012 , Page(s): 1398 - 1407
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (852 KB) |  | HTML iconHTML  

    Fibroblasts make for the most common nonmyocyte cells in the human heart and are known to play a role in structural remodeling caused by aging and various pathological states, which can eventually lead to cardiac arrhythmias and fibrillation. Gap junction formed between fibroblasts and myocytes have been recently described and were shown to alter the cardiac electrical parameters, such as action potential duration and conduction velocity, in various manners. In this study, we employed computational modeling to examine the effects of fibroblast-myocyte coupling and ratio on automaticity and electrical wave conduction during reentrant activity, with specific emphasis on dynamic phenomena and stability. Our results show that fibroblast density and coupling impact wave frequency in a biphasic way, first increasing wave frequency and then decreasing it. This can be explained by the dual role of the fibroblast cell as a current sink or a current source, depending on the coupled myocytes intracellular potential. We have also demonstrated that wave stability as manifested by the spiral-wave tip velocity and reentrant activity lifespan depends on fibroblast-myocyte coupling and ratio in a complex way. Finally, our study describes the required conditions in which spontaneous activity can occur, as a result of the fibroblasts depolarizing the myocytes' resting potential sufficiently to induce rhythmic pulses without any stimulation applied. 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