Showing papers in "IEEE Transactions on Biomedical Engineering in 2002"

An advanced detrending method with application to HRV analysis

Abstract: An advanced, simple to use, detrending method to be used before heart rate variability analysis (HRV) is presented. The method is based on smoothness priors approach and operates like a time-varying finite-impulse response high-pass filter. The effect of the detrending on time- and frequency-domain analysis of HRV is studied.

Confocal microwave imaging for breast cancer detection: localization of tumors in three dimensions

Abstract: The physical basis for breast tumor detection with microwave imaging is the contrast in dielectric properties of normal and malignant breast tissues. Confocal microwave imaging involves illuminating the breast with an ultra-wideband pulse from a number of antenna locations, then synthetically focusing reflections from the breast. The detection of malignant tumors is achieved by the coherent addition of returns from these strongly scattering objects. In this paper, we demonstrate the feasibility of detecting and localizing small (<1 cm) tumors in three dimensions with numerical models of two system configurations involving synthetic cylindrical and planar antenna arrays. Image formation algorithms are developed to enhance tumor responses and reduce early- and late-time clutter. The early-time clutter consists of the incident pulse and reflections from the skin, while the late-time clutter is primarily due to the heterogeneity of breast tissue. Successful detection of 6-mm-diameter spherical tumors is achieved with both planar and cylindrical systems, and similar performance measures are obtained. The influences of the synthetic array size and position relative to the tumor are also explored.

Design and implementation of a brain-computer interface with high transfer rates

Abstract: This paper presents a brain-computer interface (BCI) that can help users to input phone numbers. The system is based on the steady-state visual evoked potential (SSVEP). Twelve buttons illuminated at different rates were displayed on a computer monitor. The buttons constituted a virtual telephone keypad, representing the ten digits 0-9, BACKSPACE, and ENTER. Users could input phone number by gazing at these buttons. The frequency-coded SSVEP was used to judge which button the user desired. Eight of the thirteen subjects succeeded in ringing the mobile phone using the system. The average transfer rate over all subjects was 27.15 bits/min. The attractive features of the system are noninvasive signal recording, little training required for use, and high information transfer rate. Approaches to improve the performance of the system are discussed.

Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly

Abstract: A new method of evaluating the characteristics of postural transition (PT) and their correlation with falling risk in elderly people is described. The time of sit-to-stand and stand-to-sit transitions and their duration were measured using a miniature gyroscope attached to the chest and a portable recorder placed on the waist. Based on a simple model and the discrete wavelet transform, three parameters related to the PT were measured, namely, the average and standard deviation of transition duration and the occurrence of abnormal successive transitions (number of attempts to have a successful transition). The comparison between two groups of elderly subjects (with high and low fall-risk) showed that the computed parameters were significantly correlated with the falling risk as determined by the record of falls during the previous year, balance and gait disorders (Tinetti score), visual disorders, and cognitive and depressive disorders (p < 0.01). In this study, the wavelet transform has provided a powerful technique for enhancing the pattern of PT, which was mainly concentrated into the frequency range of 0.04-0.68 Hz. The system is especially adapted for long-term ambulatory monitoring of elderly people.

Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion

Abstract: This paper presents a novel approach for speckle reduction and coherence enhancement of ultrasound images based on nonlinear coherent diffusion (NCD) model. The proposed NCD model combines three different models. According to speckle extent and image anisotropy, the NCD model changes progressively from isotropic diffusion through anisotropic coherent diffusion to, finally, mean curvature motion. This structure maximally low-pass filters those parts of the image that correspond to fully developed speckle, while substantially preserving information associated with resolved-object structures. The proposed implementation algorithm utilizes an efficient discretization scheme that allows for real-time implementation on commercial systems. The theory and implementation of the new technique are presented and verified using phantom and clinical ultrasound images. In addition, the results from previous techniques are compared with the new method to demonstrate its performance.

Three-dimensional finite-element analyses for radio-frequency hepatic tumor ablation

Abstract: Radio-frequency (RF) hepatic ablation, offers an alternative method for the treatment of hepatic malignancies. We employed finite-element method (FEM) analysis to determine tissue temperature distribution during RF hepatic ablation. We constructed three-dimensional (3-D) thermal-electrical FEM models consisting of a four-tine RF probe, hepatic tissue, and a large blood vessel (10-mm diameter) located at different locations. We simulated our FEM analyses under temperature-controlled (90/spl deg/C) 8-min ablation. We also present a preliminary result from a simplified two-dimensional (2-D) FEM model that includes a bifurcated blood vessel. Lesion shapes created by the four-tine RF probe were mushroom-like, and were limited by the blood vessel. When the distance of the blood vessel was 5 mm from the nearest distal electrode 1) in the 3-D model, the maximum tissue temperature (hot spot) appeared next to electrode A. The location of the hot spot was adjacent to another electrode 2) on the opposite side when the blood vessel was 1 mm from electrode A. The temperature distribution in the 2-D model was highly nonuniform due to the presence of the bifurcated blood vessel. Underdosed areas might be present next to the blood vessel from which the tumor can regenerate.

In vitro electrical properties for iridium oxide versus titanium nitride stimulating electrodes

Abstract: Stimulating electrode materials must be capable of supplying high-density electrical charge to effectively activate neural tissue. Platinum is the most commonly used material for neural stimulation. Two other materials have been considered: iridium oxide and titanium nitride. This study directly compared the electrical characteristics of iridium oxide and titanium nitride by fabricating silicon substrate probes that differed only in the material used to form the electrode. Electrochemical measurements indicated that iridium oxide had lower impedance and a higher charge storage capacity than titanium nitride, suggesting better performance as a stimulating electrode. Direct measurement of the electrode potential in response to a biphasic current pulse confirmed that iridium oxide uses less voltage to transfer the same amount of charge, therefore using less power. The charge injection limit for titanium nitride was 0.87 mC/cm/sup 2/, contradicting other reports estimating that titanium nitride was capable of injecting 22 mC/cm/sup 2/. Iridium oxide charge storage was 4 mC/cm/sup 2/, which is comparable to other published values for iridium oxide. Electrode efficiency will lead to an overall more efficient and effective device.

Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter

Abstract: In this paper, the fitness of estimating vessel profiles with Gaussian function is evaluated and an amplitude-modified second-order Gaussian filter is proposed for the detection and measurement of vessels. Mathematical analysis is given and supported by a simulation and experiments to demonstrate that the vessel width can be measured in linear relationship with the "spreading factor" of the matched filter when the magnitude coefficient of the filter is suitably assigned. The absolute value of vessel diameter can be determined simply by using a precalibrated line, which is typically required since images are always system dependent. The experiment shows that the inclusion of the width measurement in the detection process can improve the performance of matched filter and result in a significant increase in success rate of detection.

Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm

Abstract: We developed a new image reconstruction algorithm for magnetic resonance electrical impedance tomography (MREIT). MREIT is a new EIT imaging technique integrated into magnetic resonance imaging (MRI) system. Based on the assumption that internal current density distribution is obtained using magnetic resonance imaging (MRI) technique, the new image reconstruction algorithm called J-substitution algorithm produces cross-sectional static images of resistivity (or conductivity) distributions. Computer simulations show that the spatial resolution of resistivity image is comparable to that of MRI. MREIT provides accurate high-resolution cross-sectional resistivity images making resistivity values of various human tissues available for many biomedical applications.

Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification

Abstract: We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.

A nonlinear adaptive method of elimination of power line interference in ECG signals

Abstract: A new method of elimination of power line noise in electrocardiogram signals is presented. The proposed method employs, as its main building block, a recently developed signal processing algorithm capable of extracting a specified component of a signal and tracking its variations over time. Design considerations and performance of the proposed method are presented with the aid of computer simulations. Superior performance is observed in terms of effective elimination of noise under conditions of varying powerline interference frequency. The proposed method presents a simple and robust structure which complies with practical constraints involved in the problem such as low computational resource availability and low sampling frequency.

An efficient coding algorithm for the compression of ECG signals using the wavelet transform

Abstract: A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is-insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.

Computational techniques for solving the bidomain equations in three dimensions

Abstract: The bidomain equations are the most complete description of cardiac electrical activity. Their numerical solution is, however, computationally demanding, especially in three dimensions, because of the fine temporal and spatial sampling required. This paper methodically examines computational performance when solving the bidomain equations. Several techniques to speed up this computation are examined in this paper. The first step was to recast the equations into a parabolic part and an elliptic part. The parabolic part was solved by either the finite-element method (FEM) or the interconnected cable model (ICCM). The elliptic equation was solved by FEM on a coarser grid than the parabolic problem and at a reduced frequency. The performance of iterative and direct linear equation system solvers was analyzed as well as the scalability and parallelizability of each method. Results indicate that the ICCM was twice as fast as the FEM for solving the parabolic problem, but when the total problem was considered, this resulted in only a 20% decrease in computation time. The elliptic problem could be solved on a coarser grid at one-quarter of the frequency at which the parabolic problem was solved and still maintain reasonable accuracy. Direct methods were faster than iterative methods by at least 50% when a good estimate of the extracellular potential was required. Parallelization over four processors was efficient only when the model comprised at least 500 000 nodes. Thus, it was possible to speed up solution of the bidomain equations by an order of magnitude with a slight decrease in accuracy.

Analysis and development of locomotion devices for the gastrointestinal tract

Abstract: The authors are developing devices for semi-autonomous or autonomous locomotion in the gastrointestinal (GI) tract. In this paper, they illustrate the systematic approach to the problem of "effective" locomotion in the GI tract and the critical analysis of "inchworm" locomotion devices, based on extensor and clamper mechanisms. The fundamentals of locomotion and the practical problems encountered during the development and the testing (in vitro and in vivo) of these devices are discussed. A mini device capable of propelling itself in the colon and suitable to perform, at least, rectum-sigmoidoscopy (the tract where approximately 60% of all colon cancers are found) is presented. This paper introduces preliminary, but useful, concepts for understanding, modeling and improving the performance of virtually any existing and novel devices for endoscopy of the GI tract.

Retinal vascular tree morphology: a semi-automatic quantification

Abstract: A semi-automatic method to measure and quantify geometrical and topological properties of continuous vascular trees in clinical fundus images is described. Measurements are made from binary images obtained with a previously described segmentation process. The skeletons of the segmented trees are produced by thinning, ff branch and crossing points are identified and segments of the trees are labeled and stored as a chain code. The operator selects a tree to be measured and decides if it is an arterial or venous tree. An automatic process then measures the lengths, areas and angles of the individual segments of the tree. Geometrical data and the connectivity information between branches from continuous retinal vessel trees are tabulated. A number of geometrical properties and topological indexes are derived. Vessel diameters and branching angles are validated against manual measurements and several derived geometrical and topological properties are extracted from red-free fundus images of ten normotensive and ten age- and sex-matched hypertensive subjects and compared with previously reported results.

The oral glucose minimal model: Estimation of insulin sensitivity from a meal test

Abstract: Recently, a new approach has been proposed to estimate insulin sensitivity (S/sub I/) from an oral glucose tolerance test or a meal using an "integral equation". Here, we improve on the "integral equation" by resorting to a "differential equation" approach. The classic glucose kinetics minimal model was used with the addition of a parametric model for the rate of appearance into plasma of oral glucose (Ra). Three behavioral models of Ra were proposed: piecewise-linear (P), spline (S) and dynamic (D). All three models performed satisfactorily allowing a precise estimation of S/sub I/ and a plausible reconstruction of Ra. Mean S/sub I/ estimates were virtually identical: S/sub I//sup P/=6.81/spl plusmn/0.87 (SE); S/sub I//sup S/=6.53/spl plusmn/0.80; and S/sub I//sup D/=6.62/spl plusmn/0.79. S/sub I/ strongly correlated with the integral-equation index (I) S/sub I//sup I/:r=0.99, p<0.01 for models D and S, and r=0.97, p<0.01 for P. Also, S/sub I/ compared well with insulin sensitivity estimated from intravenous glucose tolerance test in the same subjects (r=0.75, p<0.01; r=0.71, p<0.01; r=0.73, p<0.01, respectively, for P, S, and D models versus S/sub I//sup IVGTT/). Finally, the novel approach allows estimation of S/sub I/ from a shorter test (120 min): model P yielded S/sub I//sup R/=7.16/spl plusmn/1.0 (R for reduced) which correlated very well with S/sub I//sup P/ and S/sub I//sup I/ (respectively, r=0.94, p<0.01; r=0.95, p<0.01) and still satisfactorily with S/sub I//sup IVGTT/ (r=0.77, p<0.01).

Comparison of machine learning and traditional classifiers in glaucoma diagnosis

Abstract: Glaucoma is a progressive optic neuropathy with characteristic structural changes in the optic nerve head reflected in the visual field. The visual-field sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visual-field test whose output is amenable to machine learning. We compared the performance of a number of machine learning algorithms with STATPAC indexes mean deviation, pattern standard deviation, and corrected pattern standard deviation. The machine learning algorithms studied included multilayer perceptron (MLP), support vector machine (SVM), and linear (LDA) and quadratic discriminant analysis (QDA), Parzen window, mixture of Gaussian (MOG), and mixture of generalized Gaussian (MGG). MLP and SVM are classifiers that work directly on the decision boundary and fall under the discriminative paradigm. Generative classifiers, which first model the data probability density and then perform classification via Bayes' rule, usually give deeper insight into the structure of the data space. We have applied MOG, MGG, LDA, QDA, and Parzen window to the classification of glaucoma from SAP. Performance of the various classifiers was compared by the areas under their receiver operating characteristic curves and by sensitivities (true-positive rates) at chosen specificities (true-negative rates). The machine-learning-type classifiers showed improved performance over the best indexes from STATPAC. Forward-selection and backward-elimination methodology further improved the classification rate and also has the potential to reduce testing time by diminishing the number of visual-field location measurements.

A novel method for the detection of apnea and hypopnea events in respiration signals

Abstract: The monitoring of breathing dynamics is an essential diagnostic tool in various clinical environments, such as sleep diagnostics, intensive care and neonatal monitoring. This paper introduces an innovative signal classification method that is capable of on-line detection of the presence or absence of normal breathing. Four different artificial neural networks are presented for the recognition of three different patterns in the respiration signals (normal breathing, hypopnea, and apnea). Two networks process the normalized respiration signals directly, while another two use sophisticatedly preprocessed signals. The development of the networks was based on training sets from the polysomnographic records of nine different patients. The detection performance of the networks was tested and compared by using up to 8000 untrained breathing patterns from 16 different patients. The networks which classified the preprocessed respiration signals produced an average detection performance of over 90%. In the light of the moderate computational power used, the presented method is not only viable in clinical polysomnographs and respiration monitors, but also in portable devices.

Characterization of micromachined spiked biopotential electrodes

Abstract: We present the characterization of dry spiked biopotential electrodes and test their suitability to be used in anesthesia monitoring systems based on the measurement of electroencephalographic signals. The spiked electrode consists of an array of microneedles penetrating the outer skin layers. We found a significant dependency of the electrode-skin-electrode impedance (ESEI) on the electrode size (i.e., the number of spikes) and the coating material of the spikes. Electrodes larger than 3/spl times/3 mm/sup 2/ coated with Ag-AgCl have sufficiently low ESEI to be well suited for electroencephalograph (EEG) recordings. The maximum measured ESEI was 4.24 k/spl Omega/ and 87 k/spl Omega/, at 1 kHz and 0.6 Hz, respectively. The minimum ESEI was 0.65 k/spl Omega/ an 16 k/spl Omega/, at the same frequencies. The ESEI of spiked electrodes is stable over an extended period of time. The arithmetic mean of the generated DC offset voltage is 11.8 mV immediately after application on the skin and 9.8 mV after 20-30 min. A spectral study of the generated potential difference revealed that the AC part was unstable at frequencies below approximately 0.8 Hz. Thus, the signal does not interfere with a number of clinical applications using real-time EEG. Comparing raw EEG recordings of the spiked electrode with commercial Zipprep electrodes showed that both signals were similar. Due to the mechanical strength of the silicon microneedles and the fact that neither skin preparation nor electrolytic gel is required, use of the spiked electrode is convenient. The spiked electrode is very comfortable for the patient.

Dependence of induced transmembrane potential on cell density, arrangement, and cell position inside a cell system

Abstract: A nonuniform transmembrane potential (TMP) is induced on a cell membrane exposed to external electric field. If the induced TMP is above the threshold value, cell membrane becomes permeabilized in a reversible process called electropermeabilization. Studying electric potential distribution on the cell membrane gives us an insight into the effects of the electric field on cells and tissues. Since cells are always surrounded by other cells, we studied how their interactions influence the induced TMP. In the first part of our study, we studied dependence of potential distribution on cell arrangement and density in infinite cell suspensions where cells were organized into simple-cubic, body-centered cubic, and face-centered cubic lattices. In the second part of the study, we examined how induced TMP on a cell membrane is dependent on its position inside a three-dimensional cell cluster. Finally, the results for cells inside the cluster were compared to those in an infinite lattice. We used numerical analysis for the study, specifically the finite-element method (FEM). The results for infinite cell suspensions show that the induced TMP depends on both cell volume fraction and cell arrangement. We established from the results for finite volume cell clusters and layers, that there is no radial dependence of induced TMP for cells inside the cluster.

Dipole models for the EEG and MEG

Abstract: The current dipole is a widely used source model in forward and inverse electroencephalography and magnetoencephalography applications. Analytic solutions to the governing field equations have been developed for several approximations of the human head using ideal dipoles as the source model. Numeric approaches such as the finite-element and finite-difference methods have become popular because they allow the use of anatomically realistic head models and the increased computational power that they require has become readily available. Although numeric methods can represent more realistic domains, the sources in such models are an approximation of the ideal dipole. In this paper, we examine several methods for representing dipole sources in finite-element models and compare the resulting surface potentials and external magnetic field with those obtained from analytic solutions using ideal dipoles.

A system for automatic measurement of circadian activity deviations in telemedicine

Abstract: A system for the automatic measurement of the circadian activity deviations in telemedicine has been developed within the framework of a "Health Integrated Smart Home Information System" (HIS/sup 2/). HIS/sup 2/ is an experimental platform for the evaluation and the development of technologies in order to ensure the security and quality of life for patients who need home based medical monitoring. Location sensors are placed in each room of the HIS/sup 2/, allowing the monitoring of patient's successive activity phases within the patient's home environment. We proceeded with a sampling in an hourly schedule to detect weak rhythmic variations. Based on numerous measurements, we established a mean value with confidence limits. These also allowed us to define a zone within which the patient's activity is qualified to be "predictable." Alerts are set off if the patient's activity deviates from this zone.

Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates

Abstract: The influence of temporarily correlated source activities on neuromagnetic reconstruction by adaptive beamformer techniques was investigated. It is known that the spatial filter weight of an adaptive beamformer cannot perfectly block correlated signals. This causes two major influences on the reconstruction results: time course distortions and reductions in reconstructed signal intensities. Our theoretical analysis and numerical experiments both showed that the reduction in signal intensity for sources with a medium degree of correlation is small. The time-course distortion for such sources, however, may be discernible. Our analysis also showed that the magnitude correlation coefficient between two correlated sources can be accurately estimated by using the beamformer outputs. A method of retrieving the original time courses using estimated correlation coefficients was developed. Our numerical experiments demonstrated that reasonably accurate time courses can be retrieved from considerably distorted time courses even when the signal-to-noise ratio is low.

Compression depth estimation for CPR quality assessment using DSP on accelerometer signals

Abstract: Chest compression is a vital part of cardiopulmonary resuscitation (CPR). This paper demonstrates how the compression depth can be estimated using the principles of inertia navigation. The proposed method uses accelerometer sensors, one placed on the patient's chest, the other beside the patient. The acceleration-to-position conversion is performed using discrete-time digital signal processing (DSP). Instability problems due to integration are combated using a set of boundary conditions. The proposed algorithm is tested on a mannequin in harsh environments, where the patient is exposed to external forces as in a boat or car, as well as improper sensor/patient alignment. The overall performance is an estimation depth error of 4.3 mm in these environments, which is reduced to 1.6 mm in a regular, flat-floor controlled environment.

A computer-aided detection of EEG seizures in infants: a singular-spectrum approach and performance comparison

Abstract: Presents a scalp electroencephalogram (EEG) seizure detection scheme based on singular spectrum analysis (SSA) and Rissanen minimum description length (MDL) model-order selection (SSA-MDL). Preprocessing of the signals allows for the drastic reduction of the number of false alarms. Statistical performance comparison with seizure detection schemes of Gotman et al. (1997) and Liu et al. (1992) is performed on both synthetic data and real EEG seizures. Monte Carlo simulations based on synthetic infant EEG seizure data reveals some detection drawbacks on a large variety of seizure waveforms. Detection using both Monte Carlo and four real infant scalp EEG signals shows the superiority of the SSA-MDL method with an average good detection rate of >93% and false detection rate <4%.

A multiple-layer finite-element model of the surface EMG signal

Abstract: The effect of skin, muscle, fat, and bone tissue on simulated surface electromyographic (EMG) signals was examined using a finite-element model. The amplitude and frequency content of the surface potential were observed to increase when the outer layer of a homogeneous muscle model was replaced with highly resistive skin or fat tissue. The rate at which the surface potential decreased as the fiber was moved deeper within the muscle also increased. Similarly, the rate at which the surface potential decayed around the surface of the model, for a constant fiber depth, increased. When layers of subcutaneous fat of increasing thickness were then added to the model, EMG amplitude, frequency content, and the rate of decay of the surface EMG signal around the limb decreased, due to the increased distance between the electrodes and the active fiber. The influence of bone on the surface potential was observed to vary considerably, depending on its location. When located close to the surface of the volume conductor, the surface EMG signal between the bone and the source and directly over the bone increased, accompanied by a slight decrease on the side of the bone distal to the active fiber. The results emphasize the importance of distinguishing between the effects of material properties and the distance between source and electrode when considering the influence of subcutaneous tissue, and suggest possible distortions in the surface EMG signal in regions where a bone is located close to the skin surface.

A feasibility study for electrical impedance tomography as a means to monitor tissue electroporation for molecular medicine

Abstract: Molecular medicine involves the introduction of macromolecules, such as drugs or gene constructs, into specific cells of the body. Electroporation, which uses electric pulses to permeate cell membranes, is a method for achieving this. However, as with other molecular medicine procedures, it lacks a real-time mechanism to detect and control which cells have been affected. We propose and demonstrate, via computer simulation, that electrical impedance tomography has the potential for detecting and imaging electroporation of cells in tissue in real-time, thereby providing feedback for controlling electroporation.

A method for quantifying atrial fibrillation organization based on wave-morphology similarity

Abstract: A new method for quantifying the organization of single bipolar electrograms recorded in the human atria during atrial fibrillation (AF) is presented. The algorithm relies on the comparison between pairs of local activation waves (LAWs) to estimate their morphological similarity, and returns a regularity index (/spl rho/) which measures the extent of repetitiveness over time of the detected activations. The database consisted of endocardial data from a multipolar basket catheter during AF and intraatrial recordings during atrial flutter. The index showed maximum regularity (/spl rho/=1) for all atrial flutter episodes and decreased significantly when increasing AF complexity as defined by Wells (type I: /spl rho/=0.75/spl plusmn/0.23; type II: /spl rho/=0.35/spl plusmn/0.11; type III: /spl rho/=0.15/spl plusmn/0.08; P<0.01). The ability to distinguish different AF episodes was assessed by designing a classification scheme based on a minimum distance analysis, obtaining an accuracy of 85.5%. The algorithm was able to discriminate among AF types even in presence of few depolarizations as no significant /spl rho/ changes were observed by reducing the signal length down to include five LAWs. Finally, the capability to detect transient instances of AF complexity and to map the local regularity over the atrial surface was addressed by the dynamic and multisite evaluation of /spl rho/, suggesting that our algorithm could improve the understanding of AF mechanisms and become useful for its clinical treatment.

Noninvasive myocardial activation time imaging: a novel inverse algorithm applied to clinical ECG mapping data

Abstract: Linear approaches like the minimum-norm least-square algorithm show insufficient performance when it comes to estimating the activation time map on the surface of the heart from electrocardiographic (ECG) mapping data. Additional regularization has to be considered leading to a nonlinear problem formulation. The Gauss-Newton approach is one of the standard mathematical tools capable of solving this kind of problem. To our experience, this algorithm has specific drawbacks which are caused by the applied regularization procedure. In particular, under clinical conditions the amount of regularization cannot be determined clearly. For this reason, we have developed an iterative algorithm solving this nonlinear problem by a sequence of regularized linear problems. At each step of iteration, an individual L-curve is computed. Subsequent iteration steps are performed with the individual optimal regularization parameter. This novel approach is compared with the standard Gauss-Newton approach. Both methods are applied to simulated ECG mapping data as well as to single beat sinus rhythm data from two patients recorded in the catheter laboratory. The proposed approach shows excellent numerical and computational performance, even under clinical conditions at which the Gauss-Newton approach begins to break down.

Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model

Abstract: A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matrix models the spatial covariances by a function dependent on sensor distance. The temporal matrix models the temporal covariances as lag dependent. In the second part, sources are estimated given this noise model, which can be done very efficiently due to the Kronecker formulation. An application to real electroencephalogram (EEG) data shows that the noise model fits the data very well. Simulation results show that the resulting source estimates are more precise than those obtained from a standard analysis neglecting the noise covariance. In addition, the estimated standard errors of the source parameter estimates are far more precise than those obtained from a standard analysis. Finally, the source parameter standard errors are used to investigate the effects of temporal sampling. It is shown that increasing the sampling by a factor x, decreases the standard errors of all source parameters with the square root of x.