Showing papers in "IEEE Transactions on Biomedical Engineering in 1999"

EEG and MEG: forward solutions for inverse methods

Abstract: A solution of the forward problem is an important component of any method for computing the spatio-temporal activity of the neural sources of magnetoencephalography (MEG) and electroencephalography (EEG) data. The forward problem involves computing the scalp potentials or external magnetic field at a finite set of sensor locations for a putative source configuration. We present a unified treatment of analytical and numerical solutions of the forward problem in a form suitable for use in inverse methods. This formulation is achieved through factorization of the lead field into the product of the moment of the elemental current dipole source with a "kernel matrix" that depends on the head geometry and source and sensor locations, and a "sensor matrix" that models sensor orientation and gradiometer effects in MEG and differential measurements in EEG. Using this formulation and a recently developed approximation formula for EEG, based on the "Berg parameters", we present novel reformulations of the basic EEG and MEG kernels that dispel the myth that EEG is inherently more complicated to calculate than MEG. We also present novel investigations of different boundary element methods (BEMs) and present evidence that improvements over currently published BEM methods can be realized using alternative error-weighting methods. Explicit expressions for the matrix kernels for MEG and EEG for spherical and realistic head geometries are included.

ECG beat detection using filter banks

Abstract: 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.

Wavelet analysis of oscillations in the peripheral blood circulation measured by laser Doppler technique

Abstract: The wavelet transform technique, a time-frequency method with logarithmic frequency resolution, was used to analyze oscillations in human peripheral blood flow measured by laser Doppler flowmetry. The oscillations extended over a wide frequency state and their periods varied in time. Within the frequency range studied, 0.0095-1.6 Hz, five characteristic oscillations were revealed, arising from both local and central regulatory mechanisms. After the insertion of endothelium-dependent and endothelium-independent vasodilators the spectra of blood how markedly differed in the frequency interval 0.0095-0.02 Hz. In this way it was demonstrated that endothelial activity is a rhythmic process that contributes to oscillations in blood flow with a characteristic frequency of around 0.01 Hz. The study illustrates the potential of laser Doppler flowmetry combined with dynamical systems analysis for studies of both the micro- and macroscopic mechanisms of blood flow regulation in vivo.

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

Abstract: 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.

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

Abstract: 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.

Wavelet transform-based QRS complex detector

Abstract: In this paper, the authors describe a QRS complex detector based on the dyadic wavelet transform (D/sub y/WT) which is robust to time-varying QRS complex morphology and to noise. They design a spline wavelet that is suitable for QRS detection. The scales of this wavelet are chosen based on the spectral characteristics of the electrocardiogram (ECG) signal. They illustrate the performance of the D/sub y/WT-based QRS detector by considering problematic ECG signals from the American Heart Association (AHA) database. Seventy hours of data was considered. The authors also compare the performance of D/sub y/WT-based QRS detector with detectors based on Okada, Hamilton-Tompkins, and multiplication of the backward difference algorithms. From the comparison, results the authors observed that although no one algorithm exhibited superior performance in all situations, the D/sub y/WT-based detector compared well with the standard techniques. For multiform premature ventricular contractions, bigeminy, and couplets tapes, the D/sub y/WT-based detector exhibited excellent performance.

Force controlled and teleoperated endoscopic grasper for minimally invasive surgery-experimental performance evaluation

Abstract: Minimally invasive surgery generates new user interfaces which create visual and haptic distortion when compared to traditional surgery. In order to regain the tactile and kinesthetic information that is lost, a computerized force feedback endoscopic surgical grasper (FREG) was developed with computer control and a haptic user interface. The system uses standard unmodified grasper shafts and tips. The FREG can control grasping forces either by surgeon teleoperation control, or under software control. The FREG performance was evaluated using an automated palpation function (programmed series of compressions) in which the grasper measures mechanical properties of the grasped materials. The material parameters obtained from measurements showed the ability of the FREG to discriminate between different types of normal soft tissues (small bowel, lung, spleen, liver, colon, and stomach) and different kinds of artificial soft tissue replication materials (latex/silicone) for simulation purposes. In addition, subjective tests of ranking stiffness of silicone materials using the FREG teleoperation mode showed significant improvement in the performance compared to the standard endoscopic grasper. Moreover, the FREG performance was closer to the performance of the human hand than the standard endoscopic grasper. The FREG as a tool incorporating the force feedback teleoperation technology may provide the basis for application in telesurgery, clinical endoscopic surgery, surgical training, and research.

Three-dimensional electrical impedance tomography based on the complete electrode model

Abstract: In electrical impedance tomography an approximation for the internal resistivity distribution is computed based on the knowledge of the injected currents and measured voltages on the surface of the body. It is often assumed that the injected currents are confined to the two-dimensional (2-D) electrode plane and the reconstruction is based on 2-D assumptions. However, the currents spread out in three dimensions and, therefore, off-plane structures have significant effect on the reconstructed images. In this paper we propose a finite element-based method for the reconstruction of three-dimensional resistivity distributions. The proposed method is based on the so-called complete electrode model that takes into account the presence of the electrodes and the contact impedances. Both the forward and the inverse problems are discussed and results from static and dynamic (difference) reconstructions with real measurement data are given. It is shown that in phantom experiments with accurate finite element computations it is possible to obtain static images that are comparable with difference images that are reconstructed from the same object with the empty (saline filled) tank as a reference.

Detecting ventricular tachycardia and fibrillation by complexity measure

Abstract: Sinus rhythm (SR), ventricular tachycardia (VT) and ventricular fibrillation (VF) belong to different nonlinear physiological processes with different complexity. In this study, the authors present a novel, and computationally fast method to detect VT and VF, which utilizes a complexity measure suggested by Lempel and Ziv (1976). For a specific window length (i.e., the length of data segment to be analyzed), the method first generates a 0-1 string by comparing the raw electrocardiogram (ECG) data to a selected suitable threshold. The complexity measure can be obtained from the 0-1 string only using two simple operations, comparison and accumulation. When the window length is 7 s, the detection accuracy for each of SR, VT, and VF is 100% for a test set of 204 body surface records (34 SR, 85 monomorphic VT, and 85 VF). Compared with other conventional time- and frequency-domain methods, such as rate and irregularity, VF-filter leakage, and sequential hypothesis testing, the new algorithm is simple, computationally efficient, and well suited for real-time implementation in automatic external defibrillators (AED's).

New thermal wave aspects on burn evaluation of skin subjected to instantaneous heating

Abstract: Comparative studies on the well-known Pennes' equation and the newly developed thermal wave model of bioheat transfer (TWMBT) were performed to investigate the wave like behaviors of bioheat transfer occurred in thermal injury of biological bodies. The one-dimensional TWMBT in a finite medium was solved using separation of variables and the analytical solution showed distinctive wave behaviors of bioheat transfer in skin subjected to instantaneous heating. The finite difference method was used to simulate and study practical problems involved in burn injuries in which skin was stratified as three layers with various thermal physical properties. Deviations between the TWMBT and the traditional Pennes' equation imply that, for high flux heating with extremely short duration (i.e., flash fire), the TWMBT which accounts for finite thermal wave propagation may provide realistic predictions on burn evaluation. A general heat flux criterion has been established to determine when the thermal wave propagation dominates the principal heat transfer process and the TWMBT can be used for tissue temperature prediction and burn evaluation. A preliminary interpretation on the mechanisms of the wave like behaviors of heat transfer in living tissues was conducted. The application of thermal wave theory can also be possibly extended to other medical problems which involve instantaneous heating or cooling.

An advanced algorithm for solving partial differential equation in cardiac conduction

Abstract: An advanced integration method for solving reaction-diffusion-type equations for cardiac conduction is suggested. Operator splitting and adaptive time step methods were used in this method, which can significantly speed up integration while preserving accuracy.

Parametric model derivation of transfer function for noninvasive estimation of aortic pressure by radial tonometry

Abstract: Aortic pressure can be estimated noninvasively by applying a transfer function (TF) to radial tonometry signals. This study compares the performance of prior approaches, based on Fourier transform and inverted aortic-to-radial model, with direct radial-to-aortic autoregressive exogenous (ARX) model. Simultaneous invasive aortic pressure and radial tonometry pressure were recorded during rest in 39 patients in the supine position. Individual radial-aortic TF's were estimated from 20 patients, and the average TF was used to predict aortic pressures in the remaining 19 patients. The direct average TF yielded accurate aortic systolic pressure estimation (error 0.4/spl plusmn/2.9 mmHg) and good reproduction of the aortic pressure waveform (root mean squared error 2.2/spl plusmn/0.9 mmHg). The inverted reverse TF (aortic radial) yielded comparable results, while the Fourier-based TF had worse performance. Individual direct TF provided improved predictive accuracy only for indexes which are based on higher frequency components of the waveform (augmentation index, systolic time period). An ARX average TF can be used to accurately estimate central aortic pressure waveform parameters from noninvasive radial pulse tracings, and its performance is superior to previous techniques.

A computational model for tracking subsurface tissue deformation during stereotactic neurosurgery

Abstract: 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.

Impact of nonlinear heat transfer on temperature control in regional hyperthermia

Abstract: We describe an optimization process specially designed for regional hyperthermia of deep-seated tumors in order to achieve desired steady-state temperature distributions. A nonlinear three-dimensional heat transfer model based on temperature-dependent blood perfusion is applied to predict the temperature. Using linearly implicit methods in time and adaptive multilevel finite elements in space, we are able to integrate efficiently the instationary nonlinear heat equation with high accuracy. Optimal heating is obtained by minimizing an integral objective function which measures the distance between desired and model predicted temperatures. A sequence of minima is calculated from successively improved constant-rate perfusion models employing a damped Newton method in an inner iteration. We compare temperature distributions for two individual patients calculated on coarse and fine spatial grids and present numerical results of optimizations for a Sigma 60 Applicator of the BSD 2000 Hyperthermia System.

A computational model of electrical stimulation of the retinal ganglion cell

Abstract: Localized retinal electrical stimulation in blind volunteers results in discrete round visual percepts corresponding to the location of the stimulating electrode. The success of such an approach to provide useful vision depends on elucidating the neuronal target of surface electrical stimulation. To determine if electrodes preferentially stimulate ganglion cells directly below them or passing fibers from distant ganglion cells, the authors developed a compartmental model for electric field stimulation of the retinal ganglion cell (RGC). In this model a RGC is stimulated by extracellular electrical fields with active. Channels and realistic cell morphology derived directly from a neuronal tracing. Three membrane models were applied: a linear passive model, a Hodgkin-Huxley model with passive dendrites (HH), and a model composed of all active compartments (FCM) with five nonlinear ion channels. Idealized monopolar point and disk stimulating electrodes were positioned above the cell, For the HH and FCM models, the position of lowest cathodal threshold to propagate an action potential was over the soma. Brief (100 /spl mu/s) cathodic stimuli were 20% (HH with disk electrode) to 73% (FCM with point-source) more effective over the soma than over the axon. In the passive model, the axon is preferentially stimulated versus the soma. Although it may be possible to electrically stimulate RGC's near their cell body at lower thresholds than at their axon, these differences are relatively small. Alternative explanations should be sought to explain the focal perceptions observed in previously reported patient trials.

Modeling of surface myoelectric signals. I. Model implementation

Abstract: The relationships between the parameters of active motor units (MU's) and the features of surface electromyography (EMG) signals have been investigated using a mathematical model that represents the surface EMG as a summation of contributions from the single muscle fibers. Each MU has parallel fibers uniformly scattered within a cylindrical volume of specified radius embedded in an anisotropic medium. Two action potentials, each modeled as a current tripole, are generated at the neuromuscular junction, propagate in opposite directions and extinguish at the fiber-tendon endings. The neuromuscular junctions and fiber-tendon endings are uniformly scattered within regions of specified width. Muscle fiber conduction velocity and average fiber length to the right and left of the center of the innervation zone are also specified. The signal produced by MU's with different geometries and conduction velocities are superimposed. Monopolar, single differential and double differential signals are computed from electrodes placed in equally spaced locations on the surface of the muscle and are displayed as functions of any of the model's parameters. Spectral and amplitude variables and conduction velocity are estimated from the surface signals and displayed as functions of any of the model's parameters. The influence of fiber-end effects, electrode misalignment, tissue anisotropy, MU's location and geometry are discussed. Part II of this paper will focus on the simulation and interpretation of experimental signals.

Inverse electrocardiography by simultaneous imposition of multiple constraints

Abstract: The authors describe two new methods for the inverse problem of electrocardiography. Both employ regularization with multiple constraints, rather than the standard single-constraint regularization. In one method, multiple constraints on the spatial behavior of the solution are used simultaneously. In the other, spatial constraints are used simultaneously with constraints on the temporal behavior of the solution. The specific cases of two spatial constraints and one spatial and one temporal constraint are considered in detail. A new method, the L-Surface, is presented to guide the choice of the required pairs of regularization parameters. In the case when both spatial and temporal regularization are used simultaneously, there is an increased computational burden, and two methods are presented to compute solutions efficiently. The methods are verified by simulations using both dipole sources and measured canine epicardial data.

Unsupervised pattern recognition for the classification of EMG signals

Abstract: 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%.

Three machine learning techniques for automatic determination of rules to control locomotion

Abstract: Automatic prediction of gait events (e,g,, heel contact, flat foot, initiation of the swing, etc.) and corresponding profiles of the activations of muscles is important for real-time control of locomotion. This paper presents three supervised machine learning (ML) techniques for prediction of the activation patterns of muscles and sensory data, based on the history of sensory data, for walking assisted by a functional electrical stimulation (FES). Those ML's are: (1) a multilayer perceptron with Levenberg-Marquardt modification of backpropagation learning algorithm; (2) an adaptive-network-based fuzzy inference system (ANFIS); and (3) a combination of an entropy minimization type of inductive learning (IL) technique and a radial basis function (RBF) type of artificial neural network with orthogonal least squares learning algorithm. Here we show the prediction of the activation of the knee flexor muscles and the knee joint angle for seven consecutive strides based on the history of the knee joint angle and the ground reaction forces. The data used for training and testing of ML's was obtained from a simulation of walking assisted with an FES system. The ability of generating rules for an FES controller was selected as the most important criterion when comparing the ML's. Other criteria such as generalization of results, computational complexity, and learning rate mere also considered. The minimal number of rules and the most explicit and comprehensible rules were obtained by ANFIS. The best generalization was obtained by the IL and RBF network.

In vivo measurement of the oxygen saturation of retinal vessels in healthy volunteers

Abstract: A new method for the spatially resolved measurement of the oxygen saturation of retinal vessels is described. Imaging spectrometry was used for both measurements of transmission and reflectance spectra of whole blood in cuvettes as well as for fundus reflectance spectra. A model was developed for the calculation of the oxygen saturation, valid in the wavelength range between 510 nm and 586 nm, in that the internal reflectance is constant and only the transmitted light depends on layer thickness and hematocrit. Altogether 265 measurements were performed in different number at 30 eyes. In each measurement, the oxygen saturation was simultaneously determined for 193 locations along a line of 1.5 mm at the fundus. The mean oxygen saturation in retinal arteries was (92.2/spl plusmn/4.1)% and (57.9/spl plusmn/9.9)% in retinal veins. The mean retinal arterio-venous difference of the oxygen saturation was (35.1/spl plusmn/9.5)%. The venous oxygen saturation depended on distance from the optic disc. The measured mean of the arterio-venous difference of the oxygen saturation corresponded well to the value of the brain (34%). The utilization of oxygen in the temporal quadrants (inferior: 39.4/spl plusmn/10.4%) is significantly (p=0.05) higher than in the nasal quadrants (inferior: 31.3/spl plusmn/6.7%).

ECG data compression using cut and align beats approach and 2-D transforms

Abstract: A new electrocardiogram (ECG) data compression method is presented which employs a two dimensional (2-D) transform. This 2-D transform method utilizes the fact that ECG signals generally show two types of redundancies-between adjacent heartbeats and between adjacent samples. A heartbeat data sequence is cut and beat-aligned to form a 2-D data array. Any 2-D compression method can then be applied. Transform coding using the 2-D discrete cosine transform (DCT) [2-D DCT] is employed here as an example. Using selections from the MIT-BIH arrhythmia and Medtronic databases, results are presented that illustrate substantial improvement in compression ratio over one-dimensional methods for comparable percent root-mean-square difference (PRD).

Quantification of force abnormalities during passive and active-assisted upper-limb reaching movements in post-stroke hemiparesis

Abstract: The authors evaluated a method for measuring abnormal upper-limb motor performance in post-stroke hemiparetic subjects. A servomechanism (MIME) moved the forearm in simple planar trajectories, directly controlling hand position and forearm orientation. Design specifications are presented, along with system performance data during an initial test of 13 stroke subjects with a wide range of impairment levels. Performance of subjects was quantified by measuring the forces and torques between the paretic limb and the servomechanism as the subjects relaxed (passive), or attempted to generate force in the direction of movement (active). During passive movements, the more severely impaired subjects resisted movement, producing higher levels of negative work than less-impaired subjects and neurologically normal controls. During active movements, the more severely impaired subjects produced forces with larger directional errors, and were less efficient in producing work. These metrics had significant test-retest repeatability. These motor performance metrics can potentially detect smaller within-subject changes than motor function scales. This method could complement currently used measurement tools for the evaluation of subjects during recovery from stroke, or during therapeutic interventions.

Probability density of the surface electromyogram and its relation to amplitude detectors

Abstract: When the surface electromyogram (EMG) generated from constant-force, constant-angle, nonfatiguing contractions is modeled as a random process, its density is typically assumed to be Gaussian. This assumption leads to root-mean-square (RMS) processing as the maximum likelihood estimator of the EMG amplitude (where EMG amplitude is defined as the standard deviation of the random process). Contrary to this theoretical formulation, experimental work has found the signal-to-noise-ratio [(SNR), defined as the mean of the amplitude estimate divided by its standard deviation] using mean-absolute-value (MAV) processing to be superior to RMS. This paper reviews RMS processing with the Gaussian model and then derives the expected (inferior) SNR performance of MAV processing with the Gaussian model. Next, a new model for the surface EMG signal, using a Laplacian density, is presented. It is shown that the MAV processor is the maximum likelihood estimator of the EMG amplitude for the Laplacian model. SNR performance based on a Laplacian model is predicted to be inferior to that of the Gaussian model by approximately 32%. Thus, minor variations in the probability distribution of the EMG may result in large decrements in SNR performance. Lastly, experimental data from constant-force, constant-angle, nonfatiguing contractions were examined. The experimentally observed densities fell in between the theoretic Gaussian and Laplacian densities. On average, the Gaussian density best fit the experimental data, although results varied with subject. For amplitude estimation, MAV processing had a slightly higher SNR than RMS processing.

Virtual reality-based training for the diagnosis of prostate cancer

Abstract: Prostate malignancies are the second leading cause of cancer deaths among men. The most common method of detecting this disease is digital rectal examination (DRE), Current DRE training is inadequate, since the number of patients that students can practice on is limited. Furthermore, allied care personnel do not train in screening for prostate cancer. Finally, there is no objective way to follow the improvement in DRE skills for medical personnel. This paper presents a virtual reality-based simulator that addresses the above problems. The prototype consists of a PHANToM haptic interface which provides feedback to the trainee's index finger, a motion restricting board, and an SGI workstation, which renders the patient's anatomy. Four types of prostates were modeled-normal, enlarged with no tumor, incipient malignancy (single tumor), and advanced malignancy (tumor cluster). Human factors studies were conducted on both nonmedical students and urology residents in order to quantify the system usefulness. After only five minutes of training, nonmedical students had a 67% correct diagnosis rate of malignant versus nonmalignant cases. This compared with 56% for urology residents in the same trials. Subjective evaluation by the residents pointed out the need to improve the virtual prostate model realism. A control group formed of urology residents performed the same trials on a modified Merck Procar simulator. The control group scored significantly better (96% correct diagnosis of malignancies). It is concluded that the virtual prostate palpation simulator, while promising, needs significant improvement in both model realism and haptic interface hardware.

A reconstruction algorithm for electrical impedance tomography data collected on rectangular electrode arrays

Abstract: A three-dimensional reconstruction algorithm in electrical impedance imaging is presented for determining the conductivity distribution beneath the surface of a medium, given surface voltage data measured on a rectangular array of electrodes. Such an electrode configuration may be desirable for using electrical impedance tomography to detect tumors in the human breast. The algorithm is based on linearizing the conductivity about a constant value. Here, the authors describe a simple implementation of the algorithm on a four electrode-by-four-electrode array and the reconstructions obtained from numerical and experimental tank data. The results demonstrate significantly better spatial resolution in the plane of the electrodes than with respect to depth.

Electroporation therapy: a new approach for the treatment of head and neck cancer

Abstract: Electroporation can deliver exogenous molecules like drugs and genes into cells by pulsed electric fields through a temporary increase in cell membrane permeability. This effect is being used for the treatment of cancer by intratumoral injection of low dosage of an otherwise marginally effective chemotherapeutic drug, bleomycin. Application of a pulsed electric field results in substantially higher uptake of the drug and enhanced killing of the cancer cells than is possible by conventional methods. The MedPulser(R), a new treatment system for local electroporation therapy (EPT) of head and neck tumors was developed and is described in this paper. EPT with bleomycin has been found to be very effective in killing cancer cells in vitro, in mouse tumor xenografts in vivo, and in tumors in humans. Ten head and neck cancer patients with recurring or unresponsive tumors were enrolled in a Phase I/II clinical trial. Treatment of the entire turner mass in each of eight patients resulted in five complete responses confirmed by biopsy and MRI, and three partial responses (/spl ges/50% shrinkage). Two additional patients who received partial treatment of their tumor mass had local response where treated, but no overall lesion remission. Duration of the complete responses ranges from 2-10 months to date. All patients tolerated the treatment well with no significant local or systemic adverse effects.

Amplitudes and latencies of single-trial ERP's estimated by a maximum-likelihood method

Abstract: The common approach in research on event-related electroencephalogram (EEG) potentials is to assume that the trigger-related signal is always the same and can be extracted from EEG background activity by simple averaging. To check the validity of this approach and to provide more exact results, latencies and amplitudes of components have to be estimated in single trials. D.T. Pham et al. (Biometrika, vol. 74, p. 525-33, 1987) applied a maximum-likelihood approach to solve the more general model which assumes that the signal hidden in EEG background activity has the same shape and amplitude but may vary in its latency from trial to trial. Extending their method the authors present a solution in which amplitude variability is also allowed. The utility of the solution to estimate the P3 component in single trials was investigated both by extensive pseudoreal simulations and in an application to real data. The simulations showed some advantage of the method over two other methods (Woody's method and peak-picking) commonly used in event-related potentials research. Application to real data provided a plausible description of single-trial sequential effects on the amplitude of the P3 component.

Modeling the effects of electric fields on nerve fibers: influence of tissue electrical properties

Abstract: The effects of anisotropy and inhomogeneity of the electrical conductivity of extracellular tissue on excitation of nerve fibers by an extracellular point source electrode were determined by computer simulation. Analytical solutions to Poisson's equation were used to calculate potentials in anisotropic infinite homogeneous media and isotropic semi-infinite inhomogeneous media, and the net driving function was used to calculate excitation thresholds for nerve fibers. The slope and intercept of the current-distance curve in anisotropic media were power functions of the ratio and product of the orthogonal conductivities, respectively. Excitation thresholds in anisotropic media were also dependent on the orientation of the fibers, and in strongly anisotropic media (/spl sigma//sub z///spl sigma//sub xy/>4) there were reversals in the recruitment order between different diameter fibers and between fibers at different distances from the electrode. In source-free regions of inhomogeneous media (two regions of differing conductivity separated by a plane boundary), the current-distance relationship of fibers parallel to the interface was dependent only on the average conductivity, whereas in regions containing the source the current-distance relationship was dependent on the individual values of conductivity. Reversals in recruitment order between fibers at different distances from the electrode and between fibers of differing diameter were found in inhomogeneous media. The results of this simulation study demonstrate that the electrical properties of the extracellular medium can have a strong influence on the pattern of neuronal excitation generated by extracellular electric fields, and indicate the importance of tissue electrical properties in interpreting results of studies employing electrical stimulation applied in complex biological volume conductors.

Modeling of surface myoelectric signals. II. Model-based signal interpretation

Abstract: For pt. I see ibid., vol. 46, no. 7, p. 810-20 (1999). Experimental electromyogram (EMG) data from the human biceps brachii were simulated using the model described in pt. I of this work. A multichannel linear electrode array, spanning the length of the biceps, was used to detect monopolar and bipolar signals, from which double differential signals were computed, during either voluntary or electrically elicited isometric contractions. For relatively low-level voluntary contractions (10%-30% of maximum force) individual firings of three to four-different motor units were identified and their waveforms were closely approximated by the model. Motor unit parameters such as depth, size, fiber orientation and length, location of innervation and tendonous zones, propagation velocity, and source width were estimated using the model. Two applications of the model are described. The first analyzes the effects of electrode rotation with respect to the muscle fiber direction and shows the possibility of conduction velocity (CV) over- and under-estimation. The second focuses on the myoelectric manifestations of fatigue during a sustained electrically elicited contraction and the interrelationship between muscle fiber CV, spectral and amplitude variables, and the length of the depolarization zone. It is concluded that a) surface EMG detection using an electrode array, when combined with a model of signal propagation, provides a useful method for understanding the physiological and anatomical determinants of EMG waveform characteristics and b) the model provides a way for the interpretation of fatigue plots.

Use of the electrohysterogram signal for characterization of contractions during pregnancy

Abstract: This article proposes a method to evaluate the ability of the electrohysterogram signal to characterize the contractions during pregnancy, in a population with high risk of preterm deliveries. This study constitutes a first stage of a project intended to develop a monitoring system for the early diagnosis of preterm deliveries. After a proper signal denoising, the authors calculate some parameters characteristic of the extracted contractions. These contractions are then divided into classes of different physiological terms. Classical techniques of data analysis, such as principal component analysis and discriminant analysis, permit the authors to show an evolution of the contractions during pregnancy, which is different between the groups of preterm deliveries and that of deliveries at term. The authors show that, in an early term of pregnancy, they can separate the two populations: women delivering at term from women delivering preterm. The authors then show that these two kinds of pregnancy are of different evolutions. These results are encouraging, because they would permit, in a follow-up medical study, to diagnose a possible preterm delivery, as well as the proximity of the delivery.