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Biomedical Engineering, IEEE Transactions on

Issue 2 • Date Feb. 1999

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Displaying Results 1 - 13 of 13
  • Approximating dipoles from human EEG activity: the effect of dipole source configuration on dipolarity using single dipole models

    Publication Year: 1999 , Page(s): 125 - 129
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (201 KB)  

    Dipolarity is the goodness-of-fit of the observed potential distribution with one calculated using specific assumptions about the source of the electrical potential distribution. The authors used computer simulations to examine the effect of different distributions of sources on their resulting dipolarity values. Electric dipoles were placed in a head-shaped model with uniform conductivity using four different dipole configurations (randomly oriented dipoles, a uniform dipole disk layer, a dipole disk layer with uniformly distributed holes, or one with randomly oriented dipoles). The best-fitting single dipole for each configuration was calculated and the dipolarity was computed as the mean squared error of the electrical potential distributions generated by the actual dipole configuration and by the best-fitting single dipole. The simulations show that: 1) a smooth dipole layer with or without holes gives dipolarities above 99.5% even when extended over areas as large as 1256 mm 2; 2) randomly oriented dipoles under a smooth dipole layer also give dipolarities above 99.5%; and 3) randomly oriented and distributed dipoles, even if contained in a small portion of the total area, give dipolarities below 93.0%. These simulations show that inhomogeneity (holes) within a dipole disk layer per se do not lower dipolarity; rather, it is the random orientation and distribution of these dipoles which lowers dipolarity. Furthermore, dipolarity is not lowered by such randomly oriented and distributed dipoles when they are beneath a dipole disk layer. View full abstract»

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  • Glucose effectiveness and insulin sensitivity from the minimal models: consequences of undermodeling assessed by Monte Carlo simulation

    Publication Year: 1999 , Page(s): 130 - 137
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (256 KB)  

    The unlabeled (cold) minimal model (MM) and the labeled (hot) minimal model (HMM) are a powerful tool to investigate in vivo metabolism from a standard intravenous glucose tolerance test (IVGTT) or hot IVGTT (HIVGTT). They allow to estimate metabolic indexes of the glucose-insulin system, namely glucose effectiveness (GE) and insulin sensitivity (IS) (of uptake and production those of MM, and of uptake only these of HMM). Here, the consequences of the single-compartment glucose kinetics approximation used in the MM's are investigated via Monte Carlo simulation, using a physiologic reference model (RM) of the system, RM allows to generate noisy synthetic plasma concentrations of glucose, tracer glucose, and insulin during IVGTT and HIVGTT, which are then analyzed with MM and HMM. The MM and HMM CE and IS are then compared with the RM ones. Results of 400 runs show that: (1) correlation of MM GE with the RM index is weak; (2) MM IS is well correlated with the RM index, but severely underestimates it; (3) HMM clearance rate is correlated with RM clearance; and (4) HMM IS is well correlated and only slightly overestimates the RM index. These results demonstrate that GE of MM is most affected by the single-compartment approximation and the indexes of HMM are more robust than those of MM. View full abstract»

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  • Influence of electrical coupling on early after depolarizations in ventricular myocytes

    Publication Year: 1999 , Page(s): 138 - 147
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (244 KB)  

    Computer modeling is used to study the effect of electrical coupling between a myocardial zone where early after-depolarizations (EADs) can develop and the normal neighboring tissue. The effects of such coupling on EAD development and on the likelihood of EAD propagation as an ectopic beat are studied. The influence on EAD formation is investigated by approximating two partially coupled myocardial zones modeled as two active elements coupled by a junctional resistance R. For R values lower than 800 Ω cm 2, the action potentials are transmitted to the coupled element, and for R values higher than 850 Ω cm 2 they are blocked. In both ranges of R, when the electrical coupling increases, the EADs appear at more negative takeoff potentials with higher amplitudes and upstrokes. The EADs are not elicited if the electrical coupling is too high. In a separate model of two one-dimensional cardiac fiber segments partially coupled by a resistance R, critical R values exist, between 42 and 54 Ω cm 2 that facilitate EAD propagation. These results demonstrate that in myocardial zones favorable to the formation of EAD, the electrical coupling dramatically affects initiation of EAD and its spread to the neighboring tissue. View full abstract»

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  • A model-based algorithm for blood glucose control in Type I diabetic patients

    Publication Year: 1999 , Page(s): 148 - 157
    Cited by:  Papers (134)  |  Patents (178)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (209 KB)  

    A model-based-predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump. View full abstract»

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  • Dynamic updating in DIAS-NIDDM and DIAS causal probabilistic networks

    Publication Year: 1999 , Page(s): 158 - 168
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (314 KB)  

    Diabetes advisory system (DIAS) is a decision support system, which has been developed to provide advice on the amount of insulin injected by subjects with insulin-dependent diabetes mellitus (IDDM). DIAS employs a temporal causal probabilistic network (CPN) to implement a stochastic model of carbohydrate metabolism. The CPN network has recently been extended to provide also advice to subjects with noninsulin-dependent diabetes mellitus (NIDDM). However, due to increased complexity and size of the extended CPN the calculations became unfeasible. The CPN network was, therefore, simplified and a novel approach employed to generate conditional probability tables. The principles of dynamic CPNs were adopted and, in combination with the method of conditioning, learning, and forecasting, were implemented in a time- and memory-efficient way. An evaluation using experimental data was carried out to compare the original and revised DIAS implementations employing data collected by patients with IDDM, and to assess the a posteriori identifiability of model parameters in patients with NIDDM. View full abstract»

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  • Unsupervised pattern recognition for the classification of EMG signals

    Publication Year: 1999 , Page(s): 169 - 178
    Cited by:  Papers (51)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (222 KB)  

    The shapes and firing rates of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from EMG signals recorded at low to moderate force levels, it is required: i) to identify the MUAPs composing the EMG signal, ii) to classify MUAPs with similar shape, and iii) to decompose the superimposed MUAP waveforms into their constituent MUAPs. For the classification of MUAPs two different pattern recognition techniques are presented: i) an artificial neural network (ANN) technique based on unsupervised learning, using a modified version of the self-organizing feature maps (SOFM) algorithm and learning vector quantization (LVQ) and ii) a statistical pattern recognition technique based on the Euclidean distance. A total of 1213 MUAPs obtained from 12 normal subjects, 13 subjects suffering from myopathy, and 15 subjects suffering from motor neuron disease were analyzed. The success rate for the ANN technique was 97.6% and for the statistical technique 95.3%. For the decomposition of the superimposed waveforms, a technique using crosscorrelation for MUAP's alignment, and a combination of Euclidean distance and area measures in order to classify the decomposed waveforms is presented. The success rate for the decomposition procedure was 90%. View full abstract»

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  • Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network

    Publication Year: 1999 , Page(s): 179 - 185
    Cited by:  Papers (107)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB)  

    The authors have developed a method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat. Changes in QRS complexes due to rhythm origination and conduction path were observed with the Fourier transform, and three kinds of rhythms were discriminated by a neural network. In this paper, the potential of the authors' method for clinical uses and real-time detection was examined using human surface ECGs and intracardiac electrograms (EGMs). The method achieved high sensitivity and specificity (>0.98) in discrimination of supraventricular rhythms from ventricular ones. The authors also present a hardware implementation of the algorithm on a commercial single-chip CPU. View full abstract»

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  • Active cancellation system of acoustic noise in MR imaging

    Publication Year: 1999 , Page(s): 186 - 191
    Cited by:  Papers (26)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (158 KB)  

    Introduces a new neural-network architecture for reducing the acoustic noise level in magnetic resonance (MR) imaging processes. The proposed neural network (NN) consists of two cascaded time-delay NNs (TDNNs). This NN is used as the predictor of a feedback active noise control (ANC) system for reducing acoustic noises. Experimental results with real MR noises show that the proposed system achieved an average noise power attenuation of 18.75 dB, which compares favorably with previous studies. Preliminary results also show that with the proposed ANC system installed, acoustic MR noises are greatly attenuated while verbal communication during MRI sessions Is not affected. View full abstract»

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  • ECG beat detection using filter banks

    Publication Year: 1999 , Page(s): 192 - 202
    Cited by:  Papers (165)  |  Patents (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (247 KB)  

    The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters. View full abstract»

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  • Spectral analysis of periodic fluctuations in electrocardiographic repolarization

    Publication Year: 1999 , Page(s): 203 - 212
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (272 KB)  

    Repolarization alternans (RPA) indicates alternate-beat fluctuations in the temporal or spatial characteristics of the electrocardiogram (ECG) STU segment which may represent dispersion in repolarization. Spectral decomposition has revealed microvolt-level RPA which has been found to correlate with ventricular tachycardia (VT) and fibrillation, and is increasingly being used for clinical risk stratification. However, while interruptions in periodicity are known to affect spectral decomposition, their quantitative impact on RPA and its clinical utility have been poorly described. The authors therefore studied the effect of variable alignment, extrasystoles, dissimilar beats and beat exclusion on RPA magnitude in simulations and on the sensitivity and specificity of RPA for VT in a pilot clinical study, RPA magnitude was exquisitely sensitive to QRS alignment such that ±1 ms random beat misalignment reduced it by 68% in simulations. Correspondingly, suboptimal QRS alignment in clinical ECG's caused the sensitivity of RPA for inducible VT to fall from 93% to as low as 63%; while JT alignment was also less effective for RPA recovery. As an experiment in minimizing morphometric irregularities in clinical ECG's, the authors found that RPA magnitude actually fell when replacing either measurably dissimilar or ectopic beats with more representative beats. In addition, inserting or deleting beats also reduced RPA magnitude in clinical sequences and simulations. These statistical analyses suggest that the precision of beat alignment and interruptions to ECG periodicity, which may occur physiologically, may greatly reduce the clinical utility of RPA for VT. Dynamic alterations in RPA in response to sequence irregularities require further study before RPA may be optimally applied to screen for ventricular arrhythmias. View full abstract»

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  • A computational model for tracking subsurface tissue deformation during stereotactic neurosurgery

    Publication Year: 1999 , Page(s): 213 - 225
    Cited by:  Papers (77)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (772 KB)  

    Recent advances in the field of sterotactic neurosurgery have made it possible to coregister preoperative computed tomography (CT) and magnetic resonance (MR) images with instrument locations in the operating field. However, accounting for intraoperative movement of brain tissue remains a challenging problem. While intraoperative CT and MR scanners record concurrent tissue motion, there is motivation to develop methodologies which would be significantly lower in cost and more widely available. The approach the authors present is a computational model of brain tissue deformation that could be used in conjunction with a limited amount of concurrently obtained operative data to estimate subsurface tissue motion. Specifically, the authors report on the initial development of a finite element model of brain tissue adapted from consolidation theory. Validations of the computational mathematics in two and three dimensions are shown with errors of 1%-2% for the discretizations used. Experience with the computational strategy for estimating surgically induced brain tissue motion in vivo is also presented. While the predicted tissue displacements differ from measured values by about 15%, they suggest that exploiting a physics-based computational framework for updating preoperative imaging databases during the course of surgery has considerable merit. However, additional model and computational developments are needed before this approach can become a clinical reality. View full abstract»

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  • Characterization of signals and noise rejection with bipolar longitudinal intrafascicular electrodes

    Publication Year: 1999 , Page(s): 226 - 234
    Cited by:  Papers (25)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (158 KB)  

    Longitudinal intrafascicular electrodes (LIFEs) are fine electrodes threaded into the extracellular space between axons in peripheral nerves or spinal roots. The authors are developing these electrodes for application in functional electrical stimulation and in basic physiology. An area of concern in chronic recording application of LIFEs is the possibility of electromyogram and other external noise sources masking the recorded neural signals. The authors characterized neural signals recorded by LIFEs and confirmed by three independent methods that increasing interelectrode spacing for bipolar LIFEs increases signal amplitude. The spectrum of neural signal from bipolar and monopolar LIFE lies between 300 Hz and 10 kHz. The amplitude of the spectrum increases with increasing interelectrode spacing, although the distribution is not affected. Single unit analysis of LIFE recordings show that they record selectively from units closest to the electrode active site. Units with conduction velocities ranging from 50-120 m/s were identified. Extraneural noise, as stimulus artifact or electromyogram, is much reduced with bipolar LIFE recording, as compared to monopolar recordings. Relative improvement in neural signal to extraneural noise increases with interelectrode spacing up to about 2 mm. Since there is no further improvement beyond 2 mm, the authors conclude that the preferred interelectrode spacing for bipolar LIFEs is 2 mm. View full abstract»

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  • Fluid mechanics analysis of a spring-loaded jet injector

    Publication Year: 1999 , Page(s): 235 - 242
    Cited by:  Papers (6)  |  Patents (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (210 KB)  

    A syringe jet injector is a device designed to administer a drug quickly and painlessly through the skin. Though syringe jet injectors have been in use for almost 50 years, current designs still suffer from inconsistent performance. To better understand the fluid mechanics of jet injection and gain insight into how the design might influence performance, two theoretical analyses to determine the fluid pressure profile at the exit orifice were conducted. The first was a continuum analysis assuming static incompressibility. Results demonstrated that the maximum jet pressure was highly sensitive to the spring constant, initial piston velocity, and piston cross-sectional area while the time to achieve the maximum pressure was most sensitive to the injection chamber length, initial piston velocity, bulk modulus of the injectant, and the piston cross-sectional area. The second analysis was a shock wave analysis. Results demonstrated a stepwise pressure-time plot that was similar in magnitude to that for the continuum analysis assuming static incompressibility. Results from these two investigations are useful for design modification of the jet injector to achieve desired pressure-time profiles at the orifice. Control of pressure-time profiles may help to achieve a more consistent and effective injection process. 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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Bin He
Department of Biomedical Engineering