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This paper presents a set of validation procedures for nonrigid registration of functional EPI to anatomical M~ brain images. Although various registration techniques have been developed and validated for high-resolution anatomical MRI images, due to a lack of quantitative and qualitative validation procedures, the use of nonrigid registration between functional EPI and anatomical MRI images has not yet been deployed in neuroimaging studies. In this paper, the performance of a robust formulation of a nonrigid registration technique is evaluated in a quantitative manner based on simulated data and is further evaluated in a quantitative and qualitative manner based on in vivo data as compared to the commonly used rigid and affine registration techniques in the neuroimaging software packages. The nonrigid registration technique is formulated as a second-order constrained optimization problem using a free-form deformation model and mutual information similarity measure. Bound constraints, resolution level and cross-validation issues have been discussed to show the degree of accuracy and effectiveness of the nonrigid registration technique. The analyses performed reveal that the nonrigid approach provides a more accurate registration, in particular when the functional regions of interest lie in regions distorted by susceptibility artifacts. [ABSTRACT FROM AUTHOR]

Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called “artificial pancreas,” modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patient’s basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability. [ABSTRACT FROM PUBLISHER]

In this paper, an analysis of radio channel characteristics for single- and multiple-antenna bodyworn systems for use in body-to-body communications is presented. The work was based on an extensive measurement campaign conducted at 2.45 GHz representative of an indoor sweep and search scenario for fire and rescue personnel. Using maximum-likelihood estimation in conjunction with the Akaike information criterion (AIC), five candidate probability distributions were investigated and from these the κ - μ distribution was found to best describe small-scale fading observed in the body-to-body channels. Additional channel parameters such as autocorrelation and the cross-correlation coefficient between fading signal envelopes were also analyzed. Low cross correlation and small differences in mean signal levels between potential dual-branch diversity receivers suggested that the prospect of successfully implementing diversity in this type application is extremely good. Moreover, using selection combination, maximal ratio, and equal gain combining, up to 8.69-dB diversity gain can be made available when four spatially separated antennas are used at the receiver. Additional improvements in the combined envelopes through lower level crossing rates and fade durations at low signal levels were also observed. [ABSTRACT FROM AUTHOR]

Analysis of variability of fetal heart rate (FHR) is very important in prediction of the fetal wellbeing. The beat-to-beat variability is described quantitatively by the indices originated from invasive fetal electrocardiography which provides the FHR signal in a form of time event series. Today, monitoring instrumentation is based on Doppler ultrasound technology. We used two bedside fetal monitors with different processing methods for heartbeat detection and FHR signal determination: the autocorrelation and cross-correlation techniques. Both monitors provide the output signal in a form of evenly spaced samples. The goal of this paper is to present a new method for the FHR signal processing, which enables extraction of series of consecutive heartbeat intervals from the sampled signal. The proposed correction algorithms allow recognition and removal of the FHR signal distortions typical for fetal monitors-invalid and duplicated samples. The correction efficiency has been verified based on the FHR variability indices calculated for the sampled signal and the corresponding event series. For both monitors, considerable influence of the signal representation on indices values was noted. Concluding, we recommended implementing these algorithms in fetal surveillance system as a preprocessing stage for the determination of FHR variability indices. [ABSTRACT FROM AUTHOR]

This paper introduces a simple algorithm for tomographic reconstruction based on the use of a complexity regularization term. The regularization is formulated in the discrete cosine transform (DCT) domain by promoting a low-noise reconstruction having a high sparsity in the frequency domain. The resulting algorithm simply alternates between a maximum-likelihood (ML) expectation-maximization (EM) update and a decreasing sparsity constraint in the DCT domain. Applications to SPECT reconstruction and comparisons with a classical estimator using the best available regularization terms are given in order to illustrate the potential of our reconstruction technique. [ABSTRACT FROM AUTHOR]

In detecting motor related activity from mechanomyographic (MMG) recordings, the acquisition of long, continuous streams of MMG signals is typically preferred over the painstaking collection of individual, isolated contractions. However, a major challenge with continuous collection is the subsequent separation of the MMG data stream into segments representing individual contractions. This paper proposes a method for segmenting continuously recorded MMG data streams using computer vision while providing a highly reduced set of key images for rapid human expert verification. Transverse plane video recordings of functional grasp sequences were synchronized with the acquisition of MMG signals from the forearm. An automatic, vision-based algorithm exploiting skin color detection, motion estimation, and template matching provided segmentation cues for MMG signals arising from multiple grips. The automatic segmentation method tolerated extraneous hand movements, differentiated among multiple grips and estimated grip transition times. Our implementation segmented two grips with an average accuracy of 97.8 ± 4%, and up to seven grips with an accuracy of 73 ± 20%. The automatically extracted contraction initiation and termination times were within 173 ± 133 ms of the times obtained via manual segmentation. It is suggested that the proposed method would be particularly conducive to the assembly of large collections of signals for training MMG-driven prostheses. [ABSTRACT FROM AUTHOR]

A solution is proposed to the estimation of upper-limb orientation using miniature accelerometers and gyroscopes. This type of measurement device has many different possible applications, ranging from clinical use with patients presenting a number of conditions such as upper motor neuron syndrome and pathologies that give rise to loss of dexterity, to competitive sports training and virtual reality. Here we focus on a design that minimizes the number of sensors whilst delivering estimates of known accuracy over a defined frequency range. Minimizing the sensor count can make the measurement system less obtrusive, as well as minimising cost and reducing the required bandwidth if using a wireless solution. Accurate measurement of movement amplitude up to 15 Hz is required in our immediate application, namely to quantify tremor in multiple sclerosis patients. The drive for low numbers of sensors and good accuracy at higher frequencies leads to a novel design based on composite filters. The simple estimator structure also gives good insight into the fundamental accuracy limitations based on the sensors chosen. This paper defines the underlying mathematics, and quantifies performance for an estimator for shoulder, upper arm, lower arm and hand orientations. Good estimation accuracy up to 15Hz is indicated, and this with a reduced total sensor count of 18 compared to 24 that would be required for more conventional estimator architectures. [ABSTRACT FROM AUTHOR]

The electrical characteristics of the sparks produced between the active electrode and the biological tissue during electrosurgical procedures have been experimentally investigated. The results have shown that the minimum voltage required to initiate a spark depends on the applied voltage polarity resulting in electro- surgical voltage asymmetry. This voltage asymmetry is capable of producing dc levels that can result in tissue electrostimulation or direct current burns as discussed in this paper. The experimental setup and the conditions, under which the results have been obtained, including the techniques used to improve experimental reproducibility, are reported in detail. [ABSTRACT FROM AUTHOR]

In recent years, active microwave breast imaging is increasingly being viewed as a promising complementary imaging modality for cancer detection. In this paper, we present a novel deformable reflector microwave tomography technique for noninvasive characterization of the breast tissue. In contrast to conventional multitransceiver designs, the proposed technique utilizes a continuously deformable reflector with metallic coating to acquire field measurements for imaging. Computational feasibility of the proposed technique to image heterogeneous dielectric tissue property is evaluated using simplified 2-D breast models. The robustness of the deformable reflector-based tomography technique in imaging the spatial distribution of the tissue dielectric property in the presence of measurement noise is investigated using first-order Tikhonov regularization. Preliminary results obtained for the 2-D breast models appear promising and indicate further investigation of the new microwave tomography technique for breast imaging. [ABSTRACT FROM AUTHOR]

The four-electrode method is commonly used for in situ measurement of the electrical resistivity of biological tissues. In this paper, a new geometric factor between the resistivity and measured resistance using the four-electrode interface is derived in the prolate spheroidal coordinates and experimentally validated. Evaluation of the experimental results shows that the resistivities determined using both the derived geometric factor and a commercial conductivity meter are in close agreement even when the length of the immersed electrodes becomes long with respect to the inter-electrode spacing. The evaluation also shows the effect of the relative size of the sample volume when the limitation to semi-infinite volume begins to result in poor accuracy. [ABSTRACT FROM AUTHOR]

This paper explores regularization options for the ill-posed spline coefficient equations in the realistic Laplacian computation. We investigate the use of the Tikhonov regularization, truncated singular value decomposition, and the so-called λ-correction with the regularization parameter chosen by the L-curve, generalized cross-validation, quasi-optimality, and the discrepancy principle criteria. The provided range of regularization techniques is much wider than in the previous works. The improvement of the realistic Laplacian is investigated by simulations on the three-shell spherical head model. The conclusion is that the best performance is provided by the combination of the Tikhonov regularization and the generalized cross-validation criterion—a combination that has never been suggested for this task before. [ABSTRACT FROM AUTHOR]

This paper describes the design and micro-assembly process of a low-profile 3-D microelectrode array for mapping the functional organization of targeted areas of the central nervous system and for possible application in neural prostheses. The array consists of multiple planar complimentary metal-oxide-semiconductor stimulating probes and 3-D assembly components. Parylene-encapsulated gold beams supported by etch-stopped silicon braces allow the backends of the probes to be folded over to reduce the height of the array above the cortical surface. A process permitting parylene to be used at wafer level with bulk-silicon wet release has been reported. Spacers are used to fix the microassembled probes in position and are equipped with interlocking structures to facilitate the assembly process and increase yield. Four-probe 256-site 3-D arrays operate from ±5 V with an average per-channel power dissipation of 97 µW at full range stimulation with pulse widths of 100 µs at 500-Hz frequency. Thirty-two sites can be stimulated simultaneously with maximum currents of ±127 µA and a current resolution of ±1 µA. The microassembly techniques allow a variety of 3-D microstructures to be created from planar components. [ABSTRACT FROM AUTHOR]

The powering of human implants via inductive coupling has been an object of interest for the past two decades. This paper discusses some of the issues concerning a distant energy link used for supplying artificial human implants, operating at the frequency of 13.56 MHz. A procedure for the design of an energy-receiving coil is given for general applications. A design procedure is also developed, with focus on coils used for supplying human implants. The correctness of the analysis of this later design procedure has been verified by experimental results. Measurements with a human tissue simulant also show little deviation from the predictions. [ABSTRACT FROM AUTHOR]

This paper introduces parametric multichannel fusion models to exploit the different but complementary brain activity information recorded from multiple channels in order to accurately classify differential brain activity into their respective categories. A parametric weighted decision fusion model and two parametric weighted data fusion models are introduced for the classification of averaged multichannel evoked potentials (EPs). The decision fusion model combines the independent decisions of each channel classifier into a decision fusion vector and a parametric classifier is designed to determine the EP class from the discrete decision fusion vector. The data fusion models include the weighted EP-sum model in which the fusion vector is a linear combination of the multichannel EPs and the EP-concatenation model in which the fusion vector is a vector-concatenation of the multichannel EPs. The discrete Karhunen-Loeve transform (DKLT) is used to select features for each channel classifier and from each data fusion vector. The difficulty in estimating the probability density function (PDF) parameters from a small number of averaged EPs is identified and the class conditional PDFs of the feature vectors of averaged EPs are, therefore, derived in terms of the PDFs of the single-trial EPs. Multivariate parametric classifiers are developed for each fusion strategy and the performances of the different strategies are compared by classifying 14-channel EPs collected from five subjects involved in making explicit match/mismatch comparisons between sequentially presented stimuli. It is shown that the performance improves by incorporating weights in the fusion rules and that the best performance is obtained using multichannel EP concatenation. It is also noted that the fusion strategies introduced are also applicable to other problems involving the classification of multicategory multivariate signals generated from multiple sources. [ABSTRACT FROM AUTHOR]

This paper presents a method for comparing multiple circulatory waveforms measured at different locations to improve cardiovascular parameter estimation from these signals. The method identifies the distinct vascular dynamics that shape each waveform signal, and estimates the common cardiac flow input shared by them. This signal-processing algorithm uses the Laguerre function series expansion for modeling the hemodynamics of each arterial branch, and identifies unknown parameters in these models from peripheral waveforms using multichannel blind system identification. An effective technique for determining the Laguerre base pole is developed, so that the Laguerre expansion captures and quickly converges to the intrinsic arterial dynamics observed in the two circulatory signals. Furthermore, a novel deconvolution method is developed in order to stably invert the identified dynamic models for estimating the cardiac output (CO) waveform from peripheral pressure waveforms. The method is applied to experimental swine data. A mean error of less than 5% with the measured peripheral pressure waveforms has been achieved using the models and excellent agreement between the estimated CO waveforms and the gold standard measurements have been obtained. [ABSTRACT FROM AUTHOR]

This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The experiments examine the GMMs algorithmic issues including the model order selection and variance limiting, the segmentation of the data, and various feature sets including time-domain features and autoregressive features. The benefits of postprocessing the results using a majority vote rule are demonstrated. The performance of the GMM is compared to three commonly used classifiers: a linear discriminant analysis, a linear perceptron network, and a multilayer perceptron neural network. The GMM-based limb motion classification system demonstrates exceptional classification accuracy and results in a robust method of motion classification with low computational load. [ABSTRACT FROM AUTHOR]

Before undertaking new biomedical research, identifying concepts that have already been patented is essential. A traditional keyword-based search on patent databases may not be sufficient to retrieve all the relevant information, especially for the biomedical domain. This paper presents BioPatentMiner, a system that facilitates information retrieval and knowledge discovery from biomedical patents. The system first identifies biological terms and relations from the patents and then integrates the information from the patents with knowledge from biomedical ontologies to create a Semantic Web. Besides keyword search and queries linking the properties specified by one or more RDF triples, the system can discover semantic associations between the Web resources. The system also determines the importance of the resources to rank the results of a search and prevent information overload while determining the semantic associations. [ABSTRACT FROM AUTHOR]

Biotelemetric systems, especially those that employ implanted devices, work with inductive links, where usually large circular external coils are separated by relatively large distances (dimensions of centimeters) from the small/Millimeter- or submillimeter-size) implanted coils. This paper shows that order these conditions, a simplified method for calculation of the mutual (M) between the coils, avoiding elliptic integrals, can be obtained. A procedure for coil design, with maximum M between them, is also described. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set (self-paced is) of "BCI Competition 2003" with a classification accuracy of 84% on the test set. [ABSTRACT FROM AUTHOR]

This paper presents an energy ratio-based method and a wavelet-based cascaded adaptive filter (CAF) for detecting and removing baseline drift from pulse waveforms. Experiments on 50 simulated and five hundred real pulse signals demonstrate that this CAF outperforms traditional filters both in removing baseline drift and in preserving the diagnostic information of pulse waveforms. [ABSTRACT FROM AUTHOR]

The purpose of this Special Section on Grand Challenges in Engineering Life Sciences and Medicine is to review and discuss challenges and opportunities in the interface of engineering with life sciences and medicine. The special section presents the challenges and trends in the following areas: 1) education and training in biomedical engineering; 2) engineering the brain and nervous system; 3) engineering the cardiovascular system; 4) engineering approaches for cancer management; and 5) emerging engineering technologies in life sciences and medicine. Nearly 100 manuscripts were submitted in response to our Call for Papers. After an initial screening and peer review, 18 papers were selected for inclusion in the Special Section. The guest editor provides a summary of the articles included in the special issue. [ABSTRACT FROM PUBLISHER]

This article discusses about the 2004 issues of the journal "IEEE Transactions on Biomedical Engineering" (T-BME). The T-BME issues in 2004 were always on time, which means they were mailed before the first of the month. The paper backlog grew in 2004 to 120 papers, for reasons that are not totally clear to the author, but are a combination of the rapid review cycle and increase in the number of submissions to the journal. One should be grateful to the EMB society for agreeing to a 500-page budget increase in mid 2004 which trimmed the number of papers in the backlog, but unfortunately did not solve it totally. As a consequence you have Probably noticed the increased thickness of the T-BME issues.

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers. [ABSTRACT FROM PUBLISHER]