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Biomedical Engineering, IEEE Transactions on

Issue 7 • Date July 2014

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Displaying Results 1 - 25 of 37
  • [Front cover]

    Publication Year: 2014 , Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Biomedical Engineering publication information

    Publication Year: 2014 , Page(s): C2
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    Freely Available from IEEE
  • Table of contents

    Publication Year: 2014 , Page(s): 1917 - 1918
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  • Network Community Structure Detection for Directional Neural Networks Inferred From Multichannel Multisubject EEG Data

    Publication Year: 2014 , Page(s): 1919 - 1930
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1473 KB) |  | HTML iconHTML  

    In many neuroscience applications, one is interested in identifying the functional brain modules from multichannel, multiple subject neuroimaging data. However, most of the existing network community structure detection algorithms are limited to single undirected networks and cannot reveal the common community structure for a collection of directed networks. In this paper, we propose a community detection algorithm for weighted asymmetric (directed) networks representing the effective connectivity in the brain. Moreover, the issue of finding a common community structure across subjects is addressed by maximizing the total modularity of the group. Finally, the proposed community detection algorithm is applied to multichannel multisubject electroencephalogram data. View full abstract»

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  • Streaming Updates for Heart Rate Variability Algorithms

    Publication Year: 2014 , Page(s): 1931 - 1937
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1417 KB) |  | HTML iconHTML  

    Heart rate variability (HRV) quantifies the fluctuations of the lengths of consecutive heart beat intervals, and is a reliable descriptor of many physiological factors modulating the normal rhythm of the heart. As the heart rate signal is nonstationary, indicators deduced from it may be present at all times, but may also occur episodically at nonpredetermined time instances. The potential for real-time feedback long-term ambulatory recordings is thus apparent. Numerous methods for measuring HRV have been standardized and are in active use, but are typically not designed to operate at real time. In this paper, we study the most popular HRV quantification methods and propose streaming algorithms that maximally utilize previously computed information without altering the output of the methods. We demonstrate speedups of more than two orders of magnitude for typical use-case scenarios. Using our algorithms on embedded systems that compute HRV leads to dramatic decreases in power consumption and in some cases allows for computation of metrics that were not previously possible at real time. View full abstract»

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  • Focused Current Density Imaging Using Internal Electrode in Magnetic Resonance Electrical Impedance Tomography (MREIT)

    Publication Year: 2014 , Page(s): 1938 - 1946
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (871 KB) |  | HTML iconHTML  

    Magnetic resonance electrical impedance tomography (MREIT) is an imaging modality capable of visualizing cross-sectional current density and/or conductivity distributions inside an electrically conducting object. It uses an MRI scanner to measure one component of the magnetic flux density induced by an externally injected current through a pair of surface electrodes. For the cases of deep brain stimulation (DBS), electroporation, and radio frequency (RF) ablation, internal electrodes can be used to improve the quality of the MREIT images. In this paper, we propose a new MREIT imaging method using internal electrodes to visualize a current density distribution within a local region around them. To evaluate its performance, we conducted and analyzed a series of numerical simulations and phantom imaging experiments. We compared the reconstructed current density images using the internal electrodes with the obtained using only the external electrodes. We found that the proposed method using the internal electrodes stably determines the current density in the focused region with better accuracy. View full abstract»

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  • Real-Time Simulation of Three-Dimensional Shoulder Girdle and Arm Dynamics

    Publication Year: 2014 , Page(s): 1947 - 1956
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5157 KB) |  | HTML iconHTML  

    Electrical stimulation is a promising technology for the restoration of arm function in paralyzed individuals. Control of the paralyzed arm under electrical stimulation, however, is a challenging problem that requires advanced controllers and command interfaces for the user. A real-time model describing the complex dynamics of the arm would allow user-in-the-loop type experiments where the command interface and controller could be assessed. Real-time models of the arm previously described have not included the ability to model the independently controlled scapula and clavicle, limiting their utility for clinical applications of this nature. The goal of this study therefore was to evaluate the performance and mechanical behavior of a real-time, dynamic model of the arm and shoulder girdle. The model comprises seven segments linked by eleven degrees of freedom and actuated by 138 muscle elements. Polynomials were generated to describe the muscle lines of action to reduce computation time, and an implicit, first-order Rosenbrock formulation of the equations of motion was used to increase simulation step-size. The model simulated flexion of the arm faster than real time, simulation time being 92% of actual movement time on standard desktop hardware. Modeled maximum isometric torque values agreed well with values from the literature, showing that the model simulates the moment-generating behavior of a real human arm. The speed of the model enables experiments where the user controls the virtual arm and receives visual feedback in real time. The ability to optimize potential solutions in simulation greatly reduces the burden on the user during development. View full abstract»

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  • Active Adjoint Modeling Method in Microwave Induced Thermoacoustic Tomography for Breast Tumor

    Publication Year: 2014 , Page(s): 1957 - 1966
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1086 KB) |  | HTML iconHTML  

    To improve the model-based inversion performance of microwave induced thermoacoustic tomography for breast tumor imaging, an active adjoint modeling (AAM) method is proposed. It aims to provide a more realistic breast acoustic model used for tumor inversion as the background by actively measuring and reconstructing the structural heterogeneity of human breast environment. It utilizes the reciprocity of acoustic sensors, and adapts the adjoint tomography method from seismic exploration. With the reconstructed acoustic model of breast environment, the performance of model-based inversion method such as time reversal mirror is improved significantly both in contrast and accuracy. To prove the advantage of AAM, a checkerboard pattern model and anatomical realistic breast models have been used in full wave numerical simulations. View full abstract»

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  • High-Definition Transcranial Direct Current Stimulation Induces Both Acute and Persistent Changes in Broadband Cortical Synchronization: A Simultaneous tDCS–EEG Study

    Publication Year: 2014 , Page(s): 1967 - 1978
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8586 KB) |  | HTML iconHTML  

    The goal of this study was to develop methods for simultaneously acquiring electrophysiological data during high-definition transcranial direct current stimulation (tDCS) using high-resolution electroencephalography (EEG). Previous studies have pointed to the after-effects of tDCS on both motor and cognitive performance, and there appears to be potential for using tDCS in a variety of clinical applications. However, little is known about the real-time effects of tDCS on rhythmic cortical activity in humans due to the technical challenges of simultaneously obtaining electrophysiological data during ongoing stimulation. Furthermore, the mechanisms of action of tDCS in humans are not well understood. We have conducted a simultaneous tDCS-EEG study in a group of healthy human subjects. Significant acute and persistent changes in spontaneous neural activity and event-related synchronization (ERS) were observed during and after the application of high-definition tDCS over the left sensorimotor cortex. Both anodal and cathodal stimulation resulted in acute global changes in broadband cortical activity which were significantly different than the changes observed in response to sham stimulation. For the group of eight subjects studied, broadband individual changes in spontaneous activity during stimulation were apparent both locally and globally. In addition, we found that high-definition tDCS of the left sensorimotor cortex can induce significant ipsilateral and contralateral changes in event-related desynchronization and ERS during motor imagination following the end of the stimulation period. Overall, our results demonstrate the feasibility of acquiring high-resolution EEG during high-definition tDCS and provide evidence that tDCS in humans directly modulates rhythmic cortical synchronization during and after its administration. View full abstract»

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  • Assessing Dynamic Spectral Causality by Lagged Adaptive Directed Transfer Function and Instantaneous Effect Factor

    Publication Year: 2014 , Page(s): 1979 - 1988
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (525 KB) |  | HTML iconHTML  

    It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The nonzero covariance of the model's residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the “causal ordering” is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In this study, we first investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness i- time-variant spectral causality assessment was demonstrated through the mutual causality investigation of brain activity during the VEP experiments. View full abstract»

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  • Segmentation of Renal Perfusion Signals From Laser Speckle Imaging Into Clusters With Phase Synchronized Dynamics

    Publication Year: 2014 , Page(s): 1989 - 1997
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    Renal perfusion signals contain dynamics arising from the renal autoregulation feedback mechanisms as the contraction and dilation of vessels alter flow patterns. We can capture the time-varying dynamics at points across the renal surface using laser speckle imaging. We segment an imaged area of the renal cortex into clusters with phase synchronized dynamics. Our approach first uses phase coherence with a surrogate data derived threshold to identify synchronized pixel pairs. Non-negative matrix factorization is then applied to segment phase coherence estimates into phase synchronized regions. The method is applied to laser speckle imaging of the renal cortex of anaesthetized rats to identify regions on the renal surface with phase synchronized myogenic activity. In three out of six animals imaged after bolus infusion of Nω-nitro-l-arginine methyl ester (NAM), the renal surfaces are segmented into clusters with high phase coherence. No more than two clusters were identified during control period for any animal. In the remaining three animals, a strong myogenic signal could not be detected in surface perfusion during control or NAM. This method can be used to identify synchronization in renal autoregulation dynamics across the renal surface. View full abstract»

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  • Which Spring is the Best? Comparison of Methods for Virtual Stenting

    Publication Year: 2014 , Page(s): 1998 - 2010
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1197 KB) |  | HTML iconHTML  

    This paper presents a methodology for modeling the deployment of implantable devices used in minimally invasive vascular interventions. Motivated by the clinical need to perform preinterventional rehearsals of a stent deployment, we have developed methods enabling virtual device placement inside arteries, under the constraint of real-time application. This requirement of rapid execution narrowed down the search for a suitable method to the concept of a dynamic mesh. Inspired by the idea of a mesh of springs, we have found a novel way to apply it to stent modeling. The experiments conducted in this paper investigate properties of the stent models based on three different spring types: lineal, semitorsional, and torsional springs. Furthermore, this paper compares the results of various deployment scenarios for two different classes of devices: a stent graft and a flow diverter. The presented results can be of a high-potential clinical value, enabling the predictive evaluation of the outcome of a stent deployment treatment. View full abstract»

    Open Access
  • Temperature Increase in the Fetus Exposed to UHF RFID Readers

    Publication Year: 2014 , Page(s): 2011 - 2019
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (691 KB) |  | HTML iconHTML  

    Exposure to electromagnetic fields (EMFs) has prominently increased during the last decades due to the rapid development of new technologies. Among the various devices emitting EMFs, those based on Radio-frequency identification (RFID) technologies are used in all aspects of everyday life, and expose people unselectively. This scenario could pose a potential risk for some groups of the general population, such as pregnant women, who are expected to be possibly more sensitive to the thermal effects produced by EMF exposure. This is the first paper that addresses the estimation of temperature rise in two pregnant women models exposed to ultrahigh frequency RFID by computational techniques. Results show that the maximum temperature increase of the fetus and of the pregnancy-related tissues is relatively high (even about 0.7 °C), not too far from the known threshold of biological effects. However, this increase is confined to a small volume in the tissues. View full abstract»

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  • The Forward Problem of Electroarthrography: Modeling Load-Induced Electrical Potentials at the Surface of the Knee

    Publication Year: 2014 , Page(s): 2020 - 2027
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (669 KB) |  | HTML iconHTML  

    Electroarthrography (EAG) is a novel technology recently proposed to detect cartilage degradation. EAG consists of recording electrical potentials on the knee surface while the joint is undergoing compressive loading. Previous results show that these signals originating from streaming potentials in the cartilage reflect joint cartilage health. The aim of this study is to contribute to the understanding of the generation of the EAG signals and to the development of interpretation criteria using computer models of the human knee. The knee is modeled as a volume conductor composed of different regions characterized by specific electrical conductivities. The source of the EAG signal is the load-induced interstitial fluid flow that transports ions within the compressed cartilage. It is modeled as an impressed current density in different sections of the articular cartilage. The finite-element method is used to compute the potential distribution in two knee models with a realistic geometry. The simulated potential distributions correlate very well with previously measured potential values, which further supports the hypothesis that the EAG signals originate from compressed cartilage. Also, different localized cartilage defects simulated as a reduced impressed current density produce specific potential distributions that may be used to detect and localize cartilage degradation. In conclusion, given the structural and electrophysiological complexity of the knee, computer modeling constitutes an important tool to improve our understanding of the generation of EAG signals and of the various factors that affect the EAG signals so as to help develop the EAG technology as a useful clinical tool. View full abstract»

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  • Voxel-Based Dipole Orientation Constraints for Distributed Current Estimation

    Publication Year: 2014 , Page(s): 2028 - 2040
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (902 KB) |  | HTML iconHTML  

    Distributed electroencephalography source localization is a highly ill-posed problem. With measurements on the order of 102, and unknowns in the range of 104-105, the range of feasible solutions is quite large. One approach to reducing ill-posedness is to intelligently reduce the number of unknowns. Restricting solutions to gray matter is one approach. A further step is to use the anatomy of each patient to identify and constrain the orientation of the dipole within each voxel. While dipole orientation constraints for cortical patch-based approaches have been proposed, to our knowledge, no solutions for full volumetric localizations have been presented. Patch techniques account for patch surface area, but place dipoles only on the surface, rather than throughout the cortex. Variability in human cortical thickness means that thicker regions of cortex will potentially contribute more to the EEG signal, and should be accounted for in modeling. Additionally, patch models require cortical surface identification techniques, which can separate them from the extensive literature on voxel-based MR image processing, and require additional adaptation to incorporate more complex information. We present a volumetric approach for computing voxel-based distributed estimates of cortical activity with constrained dipole orientations. Using a tissue thickness estimation approach, we obtain estimates of the cortical surface normal at each voxel. These let us constrain the inverse problem, and yield localizations with reduced spatial blurring and better identification of signal magnitude within the cortex. This is demonstrated for a series of simulated and experimental data using patient-specific bioelectric models. View full abstract»

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  • Computer Simulation of Cardiac Propagation: Effects of Fiber Rotation, Intramural Conductivity, and Optical Mapping

    Publication Year: 2014 , Page(s): 2041 - 2048
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    Cardiac propagation characteristics such as aniso-tropy ratio and conduction velocities are often determined experimentally from epicardial measurements. We hypothesize that these measurements have inaccuracies due to intramural fiber rotation and transmural electrotonic interactions. We also hypothesize that optical mapping (OM) recordings compound the error, due to contributions from deeper layers. In this study, we studied propagation in a three-dimensional computer model of a slab of tissue with varying thickness and a 120° fiber rotation. Simulation results were further processed to reconstruct OM signals. As expected, simulation results demonstrated that the direction of wave propagation on the epicardial surface is not aligned with the epicardial fiber orientation. This angle difference was most pronounced for thin tissue, and decreased with decreasing intramural conductivity and increasing tissue thickness. This difference also increased with time elapsed poststimulus, as the contribution from deeper layers increased. Observations were confirmed experimentally with OM measurements from isolated rat hearts. Simulations also predicted that OM causes an additional error in measurements due to activity in deeper layers being less aligned. Several alternative approaches for the estimation of fiber orientation and anisotropy ratio were evaluated. Those based on conduction velocity measurements yielded the most accurate estimates when applied to noise-free simulated data. View full abstract»

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  • Chemotherapy Drug Scheduling for the Induction Treatment of Patients With Acute Myeloid Leukemia

    Publication Year: 2014 , Page(s): 2049 - 2056
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (885 KB) |  | HTML iconHTML  

    Leukemia is an immediately life-threatening cancer wherein immature blood cells are overproduced, accumulate in the bone marrow (BM) and blood and causes immune and blood system failure. Treatment with chemotherapy can be intensive or nonintensive and can also be life-threatening since only relatively few patient-specific and leukemia-specific factors are considered in current protocols. We have already presented a mathematical model for one intensive chemotherapy cycle with intravenous (IV) daunorubicin (DNR), and cytarabine (Ara-C) [1]. This model is now extended to nonintensive subcutaneous (SC) Ara-C and for a standard intensive chemotherapy course (four cycles), consistent with clinical practice. Model parameters mainly consist of physiological patient data, indicators of tumor burden and characteristics of cell cycle kinetics. A sensitivity analysis problem is solved and cell cycle parameters are identified to control treatment outcome. Simulation results using published cell cycle data from two acute myeloid leukemia patients [2] are presented for a course of standard treatment using intensive and nonintensive protocols. The aim of remission-induction therapy is to debulk the tumor and achieve normal BM function; by treatment completion, the total leukemic population should be reduced to at most 10$^{9}$ cells, at which point BM hypoplasia is achieved. The normal cell number should be higher than that of the leukemic, and a 3-log reduction is the maximum permissible level of population reduction. This optimization problem is formulated and solved for the two patient case studies. The results clearly present the benefits from the use of optimization as an advisory tool for treatment design. View full abstract»

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  • A Robust Algorithm for Thickness Computation at Low Resolution and Its Application to In Vivo Trabecular Bone CT Imaging

    Publication Year: 2014 , Page(s): 2057 - 2069
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1661 KB) |  | HTML iconHTML  

    Adult bone diseases, especially osteoporosis, lead to increased risk of fracture which in turn is associated with substantial morbidity, mortality, and financial costs. Clinically, osteoporosis is defined by low bone mineral density; however, increasing evidence suggests that the microarchitectural quality of trabecular bone (TB) is an important determinant of bone strength and fracture risk. Accurate measures of TB thickness and marrow spacing is of significant interest for early diagnosis of osteoporosis or treatment effects. Here, we present a new robust algorithm for computing TB thickness and marrow spacing at a low resolution achievable in vivo. The method uses a star-line tracing technique that effectively deals with partial voluming effects of in vivo imaging with voxel size comparable to TB thickness. Also, the method avoids the problem of digitization associated with conventional algorithms based on sampling distance transform along skeletons. Accuracy of the method was examined using computer-generated phantom images, while the robustness of the method was evaluated on human ankle specimens in terms of stability across a wide range of voxel sizes, repeat scan reproducibility under in vivo conditions, and correlation between thickness values computed at ex vivo and in vivo imaging resolutions. Also, the sensitivity of the method was examined by evaluating its ability to predict the bone strength of cadaveric specimens. Finally, the method was evaluated in a human study involving 40 healthy young-adult volunteers (age: 19-21 years; 20 males and 20 females) and ten athletes (age: 19- 21 years; six males and four females). Across a wide range of voxel sizes, the new method is significantly more accurate and robust as compared to conventional methods. Both TB thickness and marrow spacing measures computed using the new method demonstrated strong associations (R2 ∈ [0.83, 0.87]) with bone strength. Also, the TB thickness and marrow spacing m- asures allowed discrimination between male and female volunteers (p ∈ [0.01, 0.04]) as well as between athletes and nonathletes (p ∈ [0.005, 0.03]). View full abstract»

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  • Lasting Modulation Effects of rTMS on Neural Activity and Connectivity as Revealed by Resting-State EEG

    Publication Year: 2014 , Page(s): 2070 - 2080
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (623 KB) |  | HTML iconHTML  

    The long-lasting neuromodulatory effects of repetitive transcranial magnetic stimulation (rTMS) are of great interest for therapeutic applications in various neurological and psychiatric disorders, due to which functional connectivity among brain regions is profoundly disturbed. Classic TMS studies selectively alter neural activity in specific brain regions and observe neural activity changes on nonperturbed areas to infer underlying connectivity and its changes. Less has been indicated in direct measures of functional connectivity and/or neural network and on how connectivity/network alterations occur. Here, we developed a novel analysis framework to directly investigate both neural activity and connectivity changes induced by rTMS from resting-state EEG (rsEEG) acquired in a group of subjects with a chronic disorder of imbalance, known as the mal de debarquement syndrome (MdDS). Resting-state activity in multiple functional brain areas was identified through a data-driven blind source separation analysis on rsEEG data, and the connectivity among them was characterized using a phase synchronization measure. Our study revealed that there were significant long-lasting changes in resting-state neural activity, in theta, low alpha, and high alpha bands and neural networks in theta, low alpha, high alpha and beta bands, over broad cortical areas 4 to 5 h after the last application of rTMS in a consecutive five-day protocol. Our results of rsEEG connectivity further indicated that the changes, mainly in the alpha band, over the parietal and occipital cortices from pre- to post-TMS sessions were significantly correlated, in both magnitude and direction, to symptom changes in this group of subjects with MdDS. This connectivity measure not only suggested that rTMS can generate positive treatment effects in MdDS patients, but also revealed new potential targets for future therapeutic trials to improve treatment effects. It is promising that the new connectivity measure from- rsEEG can be used to understand the variability in treatment response to rTMS in brain disorders with impaired functional connectivity and, eventually, to determine individually tailored stimulation parameters and treatment procedures in rTMS. View full abstract»

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  • Speech Enhancement for Listeners With Hearing Loss Based on a Model for Vowel Coding in the Auditory Midbrain

    Publication Year: 2014 , Page(s): 2081 - 2091
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1091 KB) |  | HTML iconHTML  

    A novel signal-processing strategy is proposed to enhance speech for listeners with hearing loss. The strategy focuses on improving vowel perception based on a recent hypothesis for vowel coding in the auditory system. Traditionally, studies of neural vowel encoding have focused on the representation of formants (peaks in vowel spectra) in the discharge patterns of the population of auditory-nerve (AN) fibers. A recent hypothesis focuses instead on vowel encoding in the auditory midbrain, and suggests a robust representation of formants. AN fiber discharge rates are characterized by pitch-related fluctuations having frequency-dependent modulation depths. Fibers tuned to frequencies near formants exhibit weaker pitch-related fluctuations than those tuned to frequencies between formants. Many auditory midbrain neurons show tuning to amplitude modulation frequency in addition to audio frequency. According to the auditory midbrain vowel encoding hypothesis, the response map of a population of midbrain neurons tuned to modulations near voice pitch exhibits minima near formant frequencies, due to the lack of strong pitch-related fluctuations at their inputs. This representation is robust over the range of noise conditions in which speech intelligibility is also robust for normal-hearing listeners. Based on this hypothesis, a vowel-enhancement strategy has been proposed that aims to restore vowel encoding at the level of the auditory midbrain. The signal processing consists of pitch tracking, formant tracking, and formant enhancement. The novel formant-tracking method proposed here estimates the first two formant frequencies by modeling characteristics of the auditory periphery, such as saturated discharge rates of AN fibers and modulation tuning properties of auditory midbrain neurons. The formant enhancement stage aims to restore the representation of formants at the level of the midbrain by increasing the dominance of a single harmonic near each formant and saturating - hat frequency channel. A MATLAB implementation of the system with low computational complexity was developed. Objective tests of the formant-tracking subsystem on vowels suggest that the method generalizes well over a wide range of speakers and vowels. View full abstract»

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  • A Closed-Loop Brain–Computer Interface Triggering an Active Ankle–Foot Orthosis for Inducing Cortical Neural Plasticity

    Publication Year: 2014 , Page(s): 2092 - 2101
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (646 KB) |  | HTML iconHTML  

    In this paper, we present a brain-computer interface (BCI) driven motorized ankle-foot orthosis (BCI-MAFO), intended for stroke rehabilitation, and we demonstrate its efficacy in inducing cortical neuroplasticity in healthy subjects with a short intervention procedure (~15 min). This system detects imaginary dorsiflexion movements within a short latency from scalp EEG through the analysis of movement-related cortical potentials (MRCPs). A manifold-based method, called locality preserving projection, detected the motor imagery online with a true positive rate of 73.0 ± 10.3%. Each detection triggered the MAFO to elicit a passive dorsiflexion. In nine healthy subjects, the size of the motorevoked potential (MEP) elicited by transcranial magnetic stimulation increased significantly immediately following and 30 min after the cessation of this BCI-MAFO intervention for ~15 min (p = 0.009 and p <; 0.001, respectively), indicating neural plasticity. In four subjects, the size of the short latency stretch reflex component did not change following the intervention, suggesting that the site of the induced plasticity was cortical. All but one subject also performed two control conditions where they either imagined only or where the MAFO was randomly triggered. Both of these control conditions resulted in no significant changes in MEP size (p = 0.38 and p = 0.15). The proposed system provides a fast and effective approach for inducing cortical plasticity through BCI and has potential in motor function rehabilitation following stroke. View full abstract»

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  • Fast Three-Material Modeling With Triple Arch Projection for Electronic Cleansing in CTC

    Publication Year: 2014 , Page(s): 2102 - 2111
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1054 KB) |  | HTML iconHTML  

    In this paper, we propose a fast three-material modeling for electronic cleansing (EC) in computed tomographic colonography. Using a triple arch projection, our three-material modeling provides a very quick estimate of the three-material fractions to remove ridge-shaped artifacts at the T-junctions where air, soft-tissue (ST), and tagged residues (TRs) meet simultaneously. In our approach, colonic components including air, TR, the layer between air and TR, the layer between ST and TR (LST/TR), and the T-junction are first segmented. Subsequently, the material fraction of ST for each voxel in LST/TR and the T-junction is determined. Two-material fractions of the voxels in LST/TR are derived based on a two-material transition model. On the other hand, three-material fractions of the voxels in the T-junction are estimated based on our fast three-material modeling with triple arch projection. Finally, the CT density value of each voxel is updated based on our fold-preserving reconstruction model. Experimental results using ten clinical datasets demonstrate that the proposed three-material modeling successfully removed the T-junction artifacts and clearly reconstructed the whole colon surface while preserving the submerged folds well. Furthermore, compared with the previous three-material transition model, the proposed three-material modeling resulted in about a five-fold increase in speed with the better preservation of submerged folds and the similar level of cleansing quality in T-junction regions. View full abstract»

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  • Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

    Publication Year: 2014 , Page(s): 2112 - 2124
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    A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods. View full abstract»

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  • Unconstrained Sleep Apnea Monitoring Using Polyvinylidene Fluoride Film-Based Sensor

    Publication Year: 2014 , Page(s): 2125 - 2134
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1539 KB) |  | HTML iconHTML  

    We established and tested an unconstrained sleep apnea monitoring method using a polyvinylidene (PVDF) film-based sensor for continuous and accurate monitoring of apneic events occurred during sleep. Twenty-six sleep apnea patients and six normal subjects participated in this study. Subjects' respiratory signals were measured using the PVDF-based sensor during polysomnography. The PVDF sensor comprised a 4 × 1 array, and a thin silicon pad was placed over the sensor to prevent damage. Total thickness of the merged system was approximately 1.1 mm which was thin enough to prevent the subject from being consciously aware of its presence. It was designed to be placed under subjects' backs and installed between a bed cover and mattress. The proposed method was based on the standard deviation of the PVDF signals, and it was applied to a test set for detecting apneic events. The method's performance was assessed by comparing the results with a sleep physician's manual scoring. The correlation coefficient for the apnea-hypopnea index (AHI) values between the methods was 0.94 (p <; 0.001). The areas under the receiver operating curves at three AHI threshold levels (>5, >15, and >20) for sleep apnea diagnosis were 0.98, 0.99, and 0.98, respectively. For min-by-min apnea detection, the method classified sleep apnea with an average sensitivity of 72.9%, specificity of 90.6%, accuracy of 85.5%, and kappa statistic of 0.60. The developed system and method can be applied to sleep apnea detection in home or ambulatory monitoring. View full abstract»

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  • Development and Evaluation of a Prior-to-Impact Fall Event Detection Algorithm

    Publication Year: 2014 , Page(s): 2135 - 2140
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (390 KB) |  | HTML iconHTML  

    Automatic fall event detection has attracted research attention recently for its potential application in fall alarming system and wearable fall injury prevention system. Nevertheless, existing fall detection research is facing various limitations. The current study aimed to develop and validate a new fall detection algorithm using 2-D information (i.e., trunk angular velocity and trunk angle). Ten healthy elderly were involved in a laboratory study. Sagittal trunk angular kinematics was measured using inertial measurement unit during slip-induced backward falls and a variety of daily activities. The new algorithm was, on average, able to detect backward falls prior to impact, with 100% sensitivity, 95.65% specificity, and 255 ms response time. Therefore, it was concluded that the new fall detection algorithm was able to effectively detect falls during motion for the elderly population. 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