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Biomedical Engineering, IEEE Transactions on

Issue 6 • Date June 2000

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Displaying Results 1 - 15 of 15
  • Analysis of synaptic quantal depolarizations in smooth muscle using the wavelet transform

    Publication Year: 2000 , Page(s): 701 - 708
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (125 KB)  

    The time-frequency characteristics of synaptic potentials contain valuable information about the process of neurotransmission between nerves and their target organs. For example, at the synapse between autonomic nerves and smooth muscle, two central issues of neurophysiology, i.e., (1) the probability of neurotransmitter release and (2) the quantal behavior of transmission can be deduced from analysis of the rising phases of evoked excitatory junction potentials (eEJP's) recorded from smooth muscle. eEJP rising phases are marked by prominent inflexions, which reflect these features of neuronal activity, Since these inflexions contain time-varying frequency information, the authors have applied recent techniques of time-frequency analysis based upon wavelet transforms to eEJP's recorded from the guinea-pig vas deferens in vitro. They find that these techniques allow accurate and convenient characterization of neuronal release sites, and that their probability of release falls between 0.001-0.004. They have also analyzed eEJP's recorded in the presence of the chemical 1-heptanol, which reveals quantal depolarizations. These results have helped clarify the nature of the quantal depolarizations that underly eEJP's. The present method offers significant advantages over those previously employed for these tasks, and holds promise as a novel approach to the analysis of synaptic potentials. View full abstract»

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  • Adaptive whitening of the electromyogram to improve amplitude estimation

    Publication Year: 2000 , Page(s): 709 - 719
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (166 KB)  

    Previous research showed that whitening the surface electromyogram (EMG) can improve EMG amplitude estimation (where EMG amplitude is defined as the time-varying standard deviation of the EMG). However, conventional whitening via a linear filter seems to fail at low EMG amplitude levels, perhaps due to additive background noise in the measured EMG. This paper describes an adaptive whitening technique that overcomes this problem by cascading a nonadaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies, subjects used real-time EMG amplitude estimates to track a uniform-density, band-limited random target. With a 0.25-Hz bandwidth target, either adaptive whitening or multiple channel processing reduced the tracking error roughly half-way to the error achieved using the dynamometer signal as the feedback. At the 1.00-Hz bandwidth, all of the EMG processors had errors equivalent to that of the dynamometer signal, reflecting that errors in this task were dominated by subjects' inability to track targets at this bandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminish the advantages of whitening. View full abstract»

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  • Subspace averaging of steady-state visual evoked potentials

    Publication Year: 2000 , Page(s): 720 - 728
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (202 KB)  

    A new algorithm for doing signal averaging of steady-state visual evoked potentials (VEPs) is described. The subspace average is obtained by finding the orthogonal projection of the VEP measurement vector onto the signal subspace, which is based on a sinusoidal VEP signal model. The subspace average is seen to out-perform the conventional average using a new signal-to-noise-ratio-based performance measure on simulated and actual VEP data. View full abstract»

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  • Nonstationary time-series analysis applied to investigation of brainstem system dynamics

    Publication Year: 2000 , Page(s): 729 - 737
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (217 KB)  

    Previous investigations of the dynamic organization of the lower brainstem and its relation to peripheral and other central nervous systems were predominantly performed by linear methods. These are based on time-averaging algorithms, which merely can be applied to stationary signal intervals. Thus, the current concept of the common brainstem system (CBS) in the reticular formation (RF) of the lower brainstem and basic types of its functional organization have been developed. Here, the authors present experiments where neuronal activities of the RF and the nucleus tractus solitarii (NTS, first relay station of baroreceptor afferents) were recorded together with related parameters of electroencephalogram (EEG), respiration, and cardiovascular system. The RF neurons are part of the CBS, which participates in regulation and coordination of cardiovascular, respiratory, and motor systems, and vigilance. The physiological time series, thus acquired, yield information about the internal dynamic coordination of the participating regulation processes. The major problem in evaluating these data is the nonlinearity and nonstationarity of the signals. The authors used a set of especially designed time resolving methods to evaluate nonlinear dynamic couplings in the interaction between CBS neurons and cardiovascular signals, respiration and the EEG, and between NTS neurons (influenced by baroreceptor afferents) and CBS neurons. View full abstract»

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  • Decomposition of posterior alpha rhythm

    Publication Year: 2000 , Page(s): 738 - 747
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (177 KB)  

    The classical posterior alpha rhythm has been decomposed into regular and irregular components using orthogonal transformation. A periodicity generator is considered, which has three characteristic control parameters: the periodicity, the amplitudes or scaling factors and the pattern associated with successive periodic segments. The regular component is shown to be equivalent to an oscillator or periodicity generator whose parameters are dynamically varying and, thus, producing both amplitude- and frequency-modulation. The irregular component is devoid of such modulating behavior. Electroencephalogram signals from normal, maniac and epileptic subjects are studied. Through analytic signal-based analysis, it is shown that for the regular component, healthy brain possesses universal scaling behavior, whereas heterogeneous scaling or absence of universality is observed for the diseased brain. View full abstract»

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  • Uterine EMG analysis: a dynamic approach for change detection and classification

    Publication Year: 2000 , Page(s): 748 - 756
    Cited by:  Papers (22)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (164 KB)  

    Toward the goal of detecting preterm birth by characterizing events in the uterine electromyogram (EMG), the authors propose a method of detection and classification of events in this signal. Uterine EMG is considered as a nonstationary signal and the authors' approach consists of assuming piecewise stationarity and using a dynamic change detector with no a priori knowledge of the parameters of the hypotheses on the process state to be detected. The detection approach is based on the dynamic cumulative sum (DCS) of the local generalized likelihood ratios associated with a multiscale decomposition using wavelet transform. This combination of DCS and multiscale decomposition was shown to be very efficient for detection of both frequency and energy changes. An unsupervised classification based on the comparison between variance-covariance matrices computed from selected scales of the decomposition was implemented after detection. Finally a class labeling based on neural networks was developed. This algorithm of detection-classification-labeling gives satisfactory results on uterine EMG: in most cases more than 80% of the events are correctly detected and classified whatever the term of gestation. View full abstract»

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  • A new and fast nonlinear method for association analysis of biosignals

    Publication Year: 2000 , Page(s): 757 - 763
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (169 KB)  

    Presents some original theoretical aspects of a fast nonlinear association measure based on the work of Cramer (1946). The features of this new measure-the V measure-when applied to biosignals are also shown using simulated time series. A comparative study with other well-known association measures available in the literature of biosignals is presented. V was found to be twice as fast and more robust to nonlinearities than the classical cross-correlation ratio (r 2) and more than 100 times faster than the nonlinear regression coefficient (h 2), presenting similar behavior in the presence of nonlinear simulated situations. This new measure is very fast and versatile. It is appropriate to deal with nonlinear relations presenting usually a sharp peak in the association function enabling a high degree of selectivity for maxima detection. It seems to constitute an improvement over linear methods of association which is faster and more robust to the existing nonlinearities. It can be used as an alternative to more complex nonlinear association measures when computational speed is an important feature. View full abstract»

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  • A new approach for TU complex characterization

    Publication Year: 2000 , Page(s): 764 - 772
    Cited by:  Papers (36)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (146 KB)  

    Presents a new TU complex detection and characterization algorithm that consists of two stages; the first is a mathematical modeling of the electrocardiographic segment after QRS complex; the second uses classic threshold comparison techniques, over the signal and its first and second derivatives, to determine the significant points of each wave. Later, both T and U waves are morphologically classified. Amongst the principal innovations of this algorithm is the inclusion of U-wave characterization and a mathematical modeling stage, that avoids many of the problems of classic techniques when there is a low signal-to-noise ratio or when wave morphology is atypical. The results of the algorithm validation with the recently appeared QT database are also shown. For T waves these results are better when compared to other existing algorithms. U-wave results cannot be contrasted with other algorithms as, to the authors' knowledge, none are available. Examples showing the causes of principal discrepancies between the authors' algorithm and the QT database annotations are also given, and some ways of attempting to improve and benefit from the proposed algorithm are suggested. View full abstract»

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  • Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology

    Publication Year: 2000 , Page(s): 773 - 783
    Cited by:  Papers (33)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (208 KB)  

    Vibroarthrographic (VAG) signals emitted by human knee joints are nonstationary and multicomponent in nature; time-frequency distributions (TFD's) provide powerful means to analyze such signals. The objective of this paper is to construct adaptive TFD's of VAG signals suitable for feature extraction. An adaptive TFD was constructed by minimum cross-entropy optimization of the TFD obtained by the matching pursuit decomposition algorithm. Parameters of VAG signals such as energy, energy spread. frequency, and frequency spread were extracted from their adaptive TFD's. The parameters carry information about the combined TF dynamics of the signals. The mean and standard deviation of the parameters were computed, and each VAG signal was represented by a set of just six features. Statistical pattern classification experiments based on logistic regression analysis of the parameters showed an overall normal/abnormal screening accuracy of 68.9% with 90 VAG signals (51 normals and 39 abnormals), and a higher accuracy of 77.5% with a database of 71 signals with 51 normals and 20 abnormals of a specific type of patellofemoral disorder. The proposed method of VAG signal analysis is independent of joint angle and clinical information, and shows good potential for noninvasive diagnosis and monitoring of patellofemoral disorders such as chondromalacia patella. View full abstract»

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  • Modeling the cardiac action potential using B-spline surfaces

    Publication Year: 2000 , Page(s): 784 - 791
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB)  

    Presents a new method for constructing empirical, two-state-variable models of cardiac cell membrane kinetics. The formulation is based on nonuniform rational R-spline surfaces that can be manipulated interactively to produce desired action potential (AP) properties. Using this new methodology, a model of the guinea pig ventricular action potential was constructed that reproduces experimentally measured relationships between pacing cycle length and action potential duration and conduction velocity. The model is computationally efficient, requiring about sixfold less CPU time than the Beeler-Reuter ionic model and only about twice as much time as a FitzHugh-Nagumo type empirical model. Thus, for modeling propagation phenomena, this method can produce models that improve on the quantitative accuracy of both simple empirical models and elaborate ionic models, with computational cost comparable to the simplest of empirical models. View full abstract»

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  • The boundary element method in the forward and inverse problem of electrical impedance tomography

    Publication Year: 2000 , Page(s): 792 - 800
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (165 KB)  

    A new formulation of the reconstruction problem of electrical impedance tomography (EIT) is proposed. Instead of reconstructing a complete two-dimensional picture, a parameter representation of the gross anatomy is formulated, of which the optimal parameters are determined by minimizing a cost function. The two great advantages of this method are that the number of unknown parameters of the inverse problem is drastically reduced and that quantitative information of interest (e.g., lung volume) is estimated directly from the data, without image segmentation steps. The forward problem of EIT is to compute the potentials at the voltage measuring electrodes, for a given set of current injection electrodes and a given conductivity geometry. Here, it is proposed to use an improved boundary clement method (BEM) technique to solve the forward problem, in which flat boundary elements are replaced by polygonal ones. From a comparison with the analytical solution of the concentric circle model, it appears that the use of polygonal elements greatly improves the accuracy of the BEM, without increasing the computation time. In this formulation, the inverse problem is a nonlinear parameter estimation problem with a limited number of parameters. Variants of Powell's and the simplex method are used to minimize the cost function. The applicability of this solution of the EIT problem was tested in a series of simulation studies. In these studies, EIT data were simulated using a standard conductor geometry and it was attempted to find back this geometry from random starting values. In the inverse algorithm, different current injection and voltage measurement schemes and different cost functions were compared. In a simulation study, it was demonstrated that a systematic error in the assumed lung conductivity results in a proportional error in the lung cross sectional area. It appears that the authors' parametric formulation of the inverse problem leads to a stable minimization problem- - , with a high reliability, provided that the signal-to-noise ratio is about ten or higher. View full abstract»

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  • Maximum-likelihood versus maximum a posteriori parameter estimation of physiological system models: the c-peptide impulse response case study

    Publication Year: 2000 , Page(s): 801 - 811
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (254 KB)  

    Maximum-likelihood (ML), also given its connection to least squares (LS), is widely adopted in parameter estimation of physiological system models, i.e., assigning numerical values to the unknown model parameters from the experimental data. A more sophisticated but less used approach is maximum a posteriori (MAP) estimation. Conceptually, while ML adopts a Fisherian approach, i.e., only experimental measurements are supplied to the estimator, MAP estimation is a Bayesian approach, i.e., a priori available statistical information on the unknown parameters is also exploited for their estimation. Here, after a brief review of the theory behind ML and MAP estimators, the authors compare their performance in the solution of a case study concerning the determination of the parameters of a sum of exponential model which describes the impulse response of C-peptide (CP), a key substance for reconstructing insulin secretion. The results show that MAP estimation always leads to parameter estimates with a precision (sometimes significantly) higher than that obtained through ML, at the cost of only a slightly worse fit. Thus, a 3 exponential model can be adopted to describe the CP impulse response model in place of the two exponential model usually identified in the literature by the ML/LS approach. Simulated case studies are also reported to evidence the importance of taking into account a priori information in a data poor situation, e.g., when a few or too noisy measurements are available. In conclusion, the authors' results show that, when a priori information on the unknown model parameters is available, Bayes estimation can be of relevant interest, since it can significantly improve the precision of parameter estimates with respect to Fisher estimation. This may also allow the adoption of more complex models than those determinable by a Fisherian approach. View full abstract»

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  • Micromachined pipette arrays

    Publication Year: 2000 , Page(s): 812 - 819
    Cited by:  Papers (10)  |  Patents (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (147 KB)  

    The design and characterization of batch fabricated metallic micromachined pipette arrays is described. The process used to fabricate the micromachined pipette arrays (MPA) includes p + etch-stop membrane technology anisotropic etching of silicon in potassium hydroxide, sacrificial thick photoresist micromolding technology, and electrodeposition. Arrays of one to ten pipettes have been fabricated using nickel as the structural material and palladium as the biocompatible coating of inside walls. The inner dimensions of the individual pipettes fabricated to date range from 30 μm to 1.5 mm in width, 0.5 mm to several cm in length, and 550 μm in thickness. The center-to-center spacing of these pipettes varies from 100 μm to several centimeters. The MPA have a number of advantages when compared to the current micropipette technology, including the ability to transfer precise volumes of samples in the submicroliter range; the ability to manipulate samples, reagents, or buffers in a highly-parallel fashion by operating hundreds of individual pipettes simultaneously; and the compatibility with the submillimeter center-to-center dimensions of the microscale biochemical analysis systems. The application of the MPA to high lane density slab gel electrophoresis is explored. Sample wells are formed in agarose gels by using micromachined combs (solid MPA) at center-to-center spacing ranging from 250 μm to 1.9 mm. The samples are loaded using the MPA. The results of the micro-gel separations compare favorably with the standard mini-gel separations and show a twofold increase in the number of theoretical plates as well as a sixfold increase in lane density. View full abstract»

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  • Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials

    Publication Year: 2000 , Page(s): 822 - 826
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (93 KB)  

    Presents a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. The authors' method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-session variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research. View full abstract»

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  • An S1 gradient of refractoriness is not essential for reentry induction by an S2 stimulus

    Publication Year: 2000 , Page(s): 820 - 821
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (44 KB)  

    This communication reports a numerical simulation of reentry induction by successive stimulation (S1-S2) that does not require an S1 gradient of refractoriness. The S1 action potential is uniform in space, so before the S2 stimulus there is no refractory gradient. Nevertheless, a unipolar S2 stimulus initiates quatrefoil reentry. The result supports the growing realization that virtual electrodes, hyperpolarization, de-excitation, and break excitation may be important during reentry induction View full abstract»

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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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Editor-in-Chief
Bin He
Department of Biomedical Engineering