Showing papers in "IEEE Transactions on Biomedical Engineering in 2001"

Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability

Abstract: Heart rate variability (HRV) is concerned with the analysis of the intervals between heartbeats. An emerging analysis technique is the Poincare plot, which takes a sequence of intervals and plots each interval against the following interval. The geometry of this plot has been shown to distinguish between healthy and unhealthy subjects in clinical settings. The Poincare plot is a valuable HRV analysis technique due to its ability to display nonlinear aspects of the interval sequence. The problem is, how does one quantitatively characterize the plot to capture useful summary descriptors that are independent of existing HRV measures? Researchers have investigated a number of techniques: converting the two-dimensional plot into various one-dimensional views; the fitting of an ellipse to the plot shape; and measuring the correlation coefficient of the plot. The authors investigate each of these methods in detail and show that they are all measuring linear aspects of the intervals which existing HRV indexes already specify. The fact that these methods appear insensitive to the nonlinear characteristics of the intervals is an important finding because the Poincare plot is primarily a nonlinear technique. Therefore, further work is needed to determine if better methods of characterizing Poincare plot geometry can be found.

Flexible polyimide-based intracortical electrode arrays with bioactive capability

Abstract: The promise of advanced neuroprosthetic systems to significantly improve the quality of life for a segment of the deaf, blind, or paralyzed population hinges on the development of an efficacious, and safe, multichannel neural interface for the central nervous system. The candidate implantable device that is to provide such an interface must exceed a host of exacting design parameters. The authors present a thin-film, polyimide-based, multichannel intracortical Bio-MEMS interface manufactured with standard planar photo-lithographic CMOS-compatible techniques on 4-in silicon wafers. The use of polyimide provides a mechanically flexible substrate which can be manipulated into unique three-dimensional designs. Polyimide also provides an ideal surface for the selective attachment of various important bioactive species onto the device in order to encourage favorable long-term reactions at the tissue-electrode interface. Structures have an integrated polyimide cable providing efficient contact points for a high-density connector. This report details in vivo and in vitro device characterization of the biological, electrical and mechanical properties of these arrays. Results suggest that these arrays could be a candidate device for long-term neural implants.

A wavelet-based continuous classification scheme for multifunction myoelectric control

Abstract: This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.

ECG beat recognition using fuzzy hybrid neural network

Abstract: Presents the application of the fuzzy neural network for electrocardiographic (ECG) beat recognition and classification. The new classification algorithm of the ECG beats, applying the fuzzy hybrid neural network and the features drawn from the higher order statistics has been proposed in the paper. The cumulants of the second, third, and fourth orders have been used for the feature selection. The hybrid fuzzy neural network applied in the solution consists of the fuzzy self-organizing subnetwork connected in cascade with the multilayer perceptron, working as the final classifier. The c-means and Gustafson-Kessel algorithms for the self-organization of the neural network have been applied. The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution. The investigations show that the method may find practical application in the recognition and classification of different type heart beats.

EEG complexity as a measure of depth of anesthesia for patients

Abstract: A new approach for quantifying the relationship between brain activity patterns and depth of anesthesia (DOA) is presented by analyzing the spatio-temporal patterns in the electroencephalogram (EEG) using Lempel-Ziv complexity analysis. Twenty-seven patients undergoing vascular surgery were studied under general anesthesia with sevoflurane, isoflurane, propofol, or desflurane. The EEG was recorded continuously during the procedure and patients' anesthesia states were assessed according to the responsiveness component of the observer's assessment of alertness/sedation (OAA/S) score. An OAA/S score of zero or one was considered asleep and two or greater was considered awake. Complexity of the EEG was quantitatively estimated by the measure C(n), whose performance in discriminating awake and asleep states was analyzed by statistics for different anesthetic techniques and different patient populations. Compared with other measures, such as approximate entropy, spectral entropy, and median frequency, C(n) not only demonstrates better performance (93% accuracy) across all of the patients, but also is an easier algorithm to implement for real-time use. The study shows that C(n) is a very useful and promising EEG-derived parameter for characterizing the (DOA) under clinical situations.

Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series

Abstract: An integrated approach to the complexity analysis of short heart period variability series (/spl sim/300 cardiac beats) is proposed and applied to healthy subjects during the sympathetic activation induced by head-up tilt and during the driving action produced by controlled respiration (10, 15, and 20 breaths/min, CR10, CR15, and CR20 respectively). The approach relies on: 1) the calculation of Shannon entropy (SE) of the distribution of patterns lasting three beats; 2) the calculation of a regularity index based on an entropy rate (i.e., the conditional entropy); 3) the classification of frequent deterministic patterns (FDPs) lasting three beats. A redundancy reduction criterion is proposed to group FDPs in four categories according to the number and type or of heart period changes: a) no variation (0V); b) one variation (1V); and c) two like variations (2LV); 4) two unlike variations (2UV). The authors found that: 1) the SE decreased during tilt due to the increased percentage of missing patterns; 2) the regularity index increased during tilt and CR10 as patterns followed each other according to a more repetitive scheme; and 3) during CR10, SE and regularity index were not redundant as the regularity index significantly decreased while SE remained unchanged. Concerning pattern analysis the authors found that: a) at rest mainly three classes (0V, 1V, and 2LV) were detected; b) 0V patterns were more likely during tilt; c) 1V and 2LV patterns were more frequent during CR10; and d) 2UV patterns were more likely during CR20. The proposed approach based on quantification of complexity allows a full characterization of heart period dynamics and the identification of experimental conditions known to differently perturb cardiovascular regulation.

Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique

Abstract: The authors have developed a method suitable for reconstructing spatio-temporal activities of neural sources by using magnetoencephalogram (MEG) data. The method extends the adaptive beamformer technique originally proposed by Borgiotti and Kaplan (1979) to incorporate the vector beamformer formulation in which a set of three weight vectors are used to detect the source activity in three orthogonal directions. The weight vectors of the vector-extended version of the Borgiotti-Kaplan beamformer are then projected onto the signal subspace of the measurement covariance matrix to obtain the final form of the proposed beamformer's weight vectors. The authors' numerical experiments show that both spatial resolution and output signal-to-noise ratio of the proposed beamformer are significantly higher than those of the minimum-variance-based vector beamformer used in previous investigations. The authors also applied the proposed beam former to two sets of auditory-evoked MEG data, and the results clearly demonstrated the method's capability of reconstructing spatio-temporal activities of neural sources.

Artifact-resistant power-efficient design of finger-ring plethysmographic sensors

Abstract: A miniaturized, telemetric, photoplethysmograph (PPG) sensor for long-term, continuous monitoring is presented. The sensor, called a "ring sensor," is attached to a finger base for monitoring beat-to-beat pulsation, and the data is sent to a host computer via a radio-frequency transmitter. Two major design issues are addressed: one is to minimize motion artifact and the other is to minimize the consumption of battery power. An efficient double ring design is developed to lower the influence of external force, acceleration, and ambient light, and to hold the sensor gently and securely on the skin, so that the circulation at the finger may not be obstructed. Total power consumption is analyzed in relation to characteristics of individual components, sampling rate, and CPU clock speed. Optimal operating conditions are obtained for minimizing the power budget. A prototype ring sensor is designed and built based on the power budget analysis and the artifact-resistive attachment method. It is verified through experiments that the ring sensor is resistant to interfering forces and acceleration acting on the ring body. Benchmarking tests with FDA-approved PPG and electrocardiogram reveal that the ring sensor is comparable to those devices in detecting beat-to-beat pulsation despite disturbances.

Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation

Abstract: A new method for QRST cancellation is presented for the analysis of atrial fibrillation in the surface electrocardiogram (ECG). The method is based on a spatiotemporal signal model which accounts for dynamic changes in QRS morphology caused, e.g., by variations in the electrical axis of the heart. Using simulated atrial fibrillation signals added to normal ECGs, the results show that the spatiotemporal method performs considerably better than does straightforward average beat subtraction (ABS). In comparison to the ABS method, the average QRST-related error was reduced to 58 percent. The results obtained from ECGs with atrial fibrillation agreed very well with those from simulated fibrillation signals.

Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation

Abstract: The problem of the fetal electrocardiogram (FECG) extraction from maternal skin electrode measurements can be modeled from the perspective of blind source separation (BSS). Since no comparison between BSS techniques and other signal processing methods has been made, the authors compare a BSS procedure based on higher-order statistics and Widrow's multireference adaptive noise cancelling approach. As a best-case scenario for this latter method, optimal Wiener-Hopf solutions are considered. Both procedures are applied to real multichannel ECG recordings obtained from a pregnant woman. The experimental outcomes demonstrate the more robust performance of the blind technique and, in turn, verify the validity of the BSS model in this important biomedical application.

Global identifiability of nonlinear models of biological systems

Abstract: A prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. Here, the authors present a new algorithm for testing global identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global identifiability of nonlinear models of biological and physiological systems are presented.

Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills

Abstract: The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed a cholecystectomy and Nissen fundoplication in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque (F/T) sensor was used to measure the forces/torques at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly different (p<0.05) and varied based on the task being performed. High F/T magnitudes were applied by NS compared with ES while performing tissue manipulation and vice versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance of three surgeons out of the five in the ES and NS groups. The data obtained by the remaining two surgeons in each group were used for evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Preliminary data suggest that a performance index based on MM and F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education.

Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions

Abstract: The time-dependent shift in the spectral content of the surface myoelectric signal to lower frequencies has proven to be a useful tool for assessing localized muscle fatigue. Unfortunately, the technique has been restricted to constant-force, isometric contractions because of limitations in the processing methods used to obtain spectral estimates. A novel approach is proposed for calculating spectral parameters from the surface myoelectric signal during cyclic dynamic contractions. The procedure was developed using Cohen class time-frequency transforms to define the instantaneous median and mean frequency during cyclic dynamic contractions. Changes in muscle length, force, and electrode position contribute to the nonstationarity of the surface myoelectric signal. These factors, unrelated to localized fatigue, can be constrained and isolated for cyclic dynamic contractions, where they are assumed to be constant for identical phases of each cycle. Estimation errors for the instantaneous median and mean frequency are calculated from synthesized signals. It is shown that the instantaneous median frequency is affected by an error slightly lower than that related to the instantaneous mean frequency. In addition, the authors present a sample application to surface myoelectric signals recorded from the first dorsal interosseous muscle during repetitive abduction/adduction of the index finger against resistance. Results indicate that the variability of the instantaneous median frequency is related to the repeatability of the biomechanics of the exercise.

A novel approach for precise simulation of the EMG signal detected by surface electrodes

Abstract: The authors propose a new electromyogram generation and detection model. The volume conductor is described as a nonhomogeneous (layered) and anisotropic medium constituted by muscle, fat and skin tissues. The surface potential detected in space domain is obtained from the application of a two-dimensional spatial filter to the input current density source. The effects of electrode configuration, electrode size and inclination of the fibers with respect to the detection system are included in the transfer function of the filter. Computation of the signal in space domain is performed by applying the Radon transform; this permits to draw considerations about spectral dips and clear misunderstandings in previous theoretical derivations. The effects of generation and extinction of the action potentials at the fiber end plate and at the tendons are included by modeling the source current, without any approximation of its shape, as a function of space and time and by using again the Radon transform. The approach, based on the separation of the temporal and spatial properties of the muscle fiber action potential and of the volume conductor, includes the capacitive tissue properties.

Single-unit neural recording with active microelectrode arrays

Abstract: Discusses the single-unit recording characteristics of microelectrode arrays containing on-chip signal processing circuitry. Probes buffered using on-chip unity-gain operational amplifiers provide an output resistance of 200 /spl Omega/ with an input-referred noise of 11-/spl mu/V root-mean-square (rms) (100 Hz-10 kHz). Simultaneous in vivo recordings from single neurons using buffered and unbuffered (passive) iridium recording sites separated by less than 20 /spl mu/m have shown that the use of on-chip circuitry does not significantly degrade system noise. Single-unit neural activity has also been studied using probes containing closed-loop preamplifiers having a voltage gain of 40 dB and a bandwidth of 13 kHz, and several input de-baseline stabilization techniques have been evaluated. Low-noise in vivo recordings with a multiplexed probe have been demonstrated for the first time using an external asymmetrical clock running at 200 kHz. The multiplexed system adds less than 8-/spl mu/V rms of noise to the recorded signals, suppressing the 5-V clock transitions to less than 2 ppm.

Geometrical aspects of the interindividual variability of multilead ECG recordings

Abstract: The ECG as measured from healthy subjects shows a considerable interindividual variability. This variability is caused by geometrical as well as by physiological factors. In this study, the relative contribution of the geometrical factors is estimated. In addition a method aimed at correcting for these factors is described. First, a measure (RV) for quantifying the overall variability is presented, and for healthy individuals its value is estimated as 0.52. Next, based on a simulation study using the individual (heart-lung-torso) geometry of 25 subjects, the variability caused by geometrical factors is estimated as 0.40, indicating that in healthy subjects the RV for healthy individuals resulting from electrophysiology is of the order of 0.33. In an evaluation of the correction procedure, applied to realistic, simulated body surface potentials, it is shown that RV caused by geometrical factors can be reduced from 0.40 to 0.06. When applying the correction procedure to measured ECG data no reduction of the RV value could be demonstrated. These results indicate that the involved procedure of the inverse computation of a cardiac equivalent source, at the present time, is of insufficient quality to cash in on the substantial reduction of RV values from 0.52 down to 0.33 that might be obtainable.

Computer-assisted sleep staging

Abstract: To address the subjectivity in manual scoring of polysomnograms, a computer-assisted sleep staging method is presented in this paper. The method uses the principles of segmentation and self-organization (clustering) based on primitive sleep-related features to find the pseudonatural stages present in the record. Sample epochs of these natural stages are presented to the user, who can classify them according to the Rechtschaffen and Kales (RK) or any other standard. The method then learns from these samples to complete the classification. This step allows the active participation of the operator in order to customize the staging to his/her preferences. The method was developed and tested using 12 records of varying types (normal, abnormal, male, female, varying age groups). Results showed an overall concurrence of 80.6% with manual scoring of 20-s epochs according to RK standard. The greatest amount of errors occurred in the identification of the highly transitional Stage 1, 54% of which was misclassified into neighboring stages 2 or Wake.

CMOS neurostimulation ASIC with 100 channels, scaleable output, and bidirectional radio-frequency telemetry

Abstract: 100-channel neurostimulation circuit comprising a complementary metal oxide semiconductor (CMOS), application-specific integrated circuit (ASIC) has been designed, constructed and tested. The ASIC forms a significant milestone and an integral component of a 100-electrode neurostimulation system being developed by the authors. The system comprises an externally worn transmitter and a body implantable stimulator. The purpose of the system is to communicate both data and power across tissue via radio-frequency (RF) telemetry such that externally programmable, constant current, charge balanced, biphasic stimuli may be delivered to neural tissue at 100 unique sites. An intrinsic reverse telemetry feature of the ASIC has been designed such that information pertaining to the device function, reconstruction of the stimulation voltage waveform, and the measurement of impedance may be obtained through noninvasive means. To compensate for the paucity of data pertaining to the stimulation thresholds necessary in evoking a physiological response, the ASIC has been designed with scaleable current output. The ASIC has been designed primarily as a treatment of degenerative disorders of the retina whereby the 100 channels are to be utilized in the delivery of a pattern of stimuli of varying intensity and or duty cycle to the surviving neural tissue of the retina. However, it is conceivable that other fields of neurostimulation such as cochlear prosthetics and functional electronic stimulation may benefit from the employment of the system.

Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy

Abstract: The authors hypothesized that frequency domain analysis of an interatrial atrial fibrillation (AF) electrogram would show a correlation of the variance of the signal and the amplitude of harmonic peaks with the periodicity and morphology (organization) of the AF signal and defibrillation efficacy. The authors sought to develop an algorithm that would provide a high-resolution measurement of the changes in the spatiotemporal organization of AF. AF was initiated with burst atrial pacing in ten dogs. The atrial defibrillation threshold (ADFT/sub 50/) was determined, and defibrillation was repeated at the ADFT/sub 50/. Bipolar electrograms from the shocking electrodes were acquired immediately preshock, digitally filtered, and a FFT was performed. The organization index (OI) was calculated as the ratio of the area under the first four harmonic peaks to the total area of the spectrum. For a 4-s window, the mean OI was 0.505/spl plusmn/0.087 for successful shocks, versus 0.352/spl plusmn/0.068 for unsuccessful shocks (p<0.001). Receiver operator characteristic (ROC) curve analysis was used to determine the optimal sampling window for predicting successful shocks. The area of the ROC curve was 0.8 for a 1-s window, and improved to 0.9 for a 4-s window. The authors conclude that the spectrum of an AF signal contains information relating to its organization, and can be used in predicting a successful defibrillation.

A new method for pulse oximetry possessing inherent insensitivity to artifact

Abstract: A new method for pulse oximetry is presented that possesses an inherent insensitivity to corruption by motion artifact, a primary limitation in the practical accuracy and clinical applicability of current technology. Artifact corruption of the underlying photoplethysmographic signals is reduced in real time, using an electronic processing methodology that is based upon inversion of a physical artifact model. This fundamental approach has the potential to provide uninterrupted output and superior accuracy under conditions of sustained subject motion, therefore, widening the clinical scope of this useful measurement. A new calibration technique for oxygen saturation is developed for use with these processed signals, which is shown to be a generalization of the classical Interpretation. The detailed theoretical and practical issues of Implementation are then explored, highlighting important engineering simplifications implicit in this new approach. A quantitative investigation of the degree of insensitivity to artifact is also undertaken, with the aid of a custom electronic system and commercial pulse oximeter probes, which is compared and contrasted with the performance of a conventional implementation. It is demonstrated that this new methodology results in a reduced sensitivity to common classes of motion artifact, while retaining the generality to be combined with conventional signal processing techniques.

A novel optical imaging method for the early detection, quantitative grading, and mapping of cancerous and precancerous lesions of cervix

Abstract: Describes a novel optical imaging method for the in vivo early detection, quantitative staging, and mapping of cervical cancer and precancer. A multispectral imaging system was developed, which is capable of performing time-resolved imaging spectroscopy. The system was used in order to assess quantitatively the alterations in the light scattering properties of the cervix, induced selectively and reversibly in cervical neoplasias, after the application of acetic acid solution. Spectral imaging and analysis of cervix show that the maximum contrast between acetic acid responsive and nonresponsive areas is obtained at 525/spl plusmn/15 nm, which is further enhanced by cutting off the regular component of tissue reflection, with the aid of two linear cross polarizers. Successive snapshot imaging at this spectral band enables the quantitative assessment of the temporal alterations in the intensity of the backscattered light, in any spatial location of the examined area. Initial clinical trials show that optical contrast enhancement results in a notable improvement of the sensitivity in detecting incipient lesions. It was also shown that the measured temporal characteristics of the phenomenon contain specific information, which enables the differentiation between neoplastic and nonneoplastic lesions, as well as between neoplasias of different grade. The demonstrated improved sensitivity and specificity highlight the potential of the method in both clinical research and noninvasive diagnosis.

Balance prosthesis based on micromechanical sensors using vibrotactile feedback of tilt

Abstract: A prototype balance prosthesis has been made using miniature, high-performance inertial sensors to measure lateral head tilt and vibrotactile elements mounted on the body to display head tilt to the user. The device has been used to study the feasibility of providing artificial feedback of head tilt to reduce postural sway during quiet standing using six healthy subjects. Two vibrotactile display schemes were used: one in which the individual vibrating elements, called tactors, were placed on the shoulders (shoulder tactors); another in which columns of tactors were placed on the right and left sides of the trunk (side tactors). Root-mean-square head-tilt angle (Tilt) and center of pressure displacement (Sway) were measured for normal subjects standing in a semi-tandem Romberg position with eyes closed, under four conditions: no balance aids; shoulder tactors; side tactors; and light touch. Compared with no balance aids, the side tactors significantly reduced Tilt (35%) and Sway (33%). Shoulder tactors also significantly reduced Tilt (44%) and Sway (17%). Compared with tactors, light touch resulted in less Sway, but more Tilt. The results suggest that healthy normal subjects can reduce their lateral postural sway using head tilt information as provided by a vibrotactile display. Thus, further testing with balance-impaired subjects is now warranted.

An implantable power supply with an optically rechargeable lithium battery

Abstract: A novel power supply for medical implants has been developed. A wireless near-infrared power transmission recharges a lithium secondary battery in the power supply. A photovoltaic cell array embedded under skin receives near-infrared light through the skin and charges the battery directly powering an implanted device. The authors have shown that, for a photodiode area of 2.1 cm/sup 2/, 17 min of near-infrared irradiation at a 810-mn wavelength with a power density of 22 mW/cm/sup 2/ can send enough energy to allow regular commercial cardiac pacemakers to run for 24 h. The temperature rise of the skin during the light irradiation was 1.4/spl deg/C.

Optimal modeling of corneal surfaces with Zernike polynomials

Abstract: Zernike polynomials are often used as an expansion of corneal height data and for analysis of optical wavefronts. Accurate modeling of corneal surfaces with Zernike polynomials involves selecting the order of the polynomial expansion based on the measured data. The authors have compared the efficacy of various classical model order selection techniques that can be utilized for this purpose, and propose an approach based on the bootstrap. First, it is shown in simulations that the bootstrap method outperforms the classical model order selection techniques. Then, it is proved that the bootstrap technique is the most appropriate method in the context of fitting Zernike polynomials to corneal elevation data, allowing objective selection of the optimal number of Zernike terms. The process of optimal fitting of Zernike polynomials to corneal elevation data is discussed and examples are given for normal corneas and for abnormal corneas with significant distortion. The optimal model order varies as a function of the diameter of the cornea.

Computational modeling of three-dimensional microwave tomography of breast cancer

Abstract: The microwave tomographic approach is proposed to detect and image breast cancers. Taking into account the big difference in dielectrical properties between normal and malignant tissues, the authors have proposed using the microwave tomographic method to image a human breast. Because of the anatomical features of the objects, this case has to be referred to the tomography with a limited angle of observation. As a result of computer experiments the authors have established that multiview cylindrical configurations are able to provide microwave tomograms of the breast with a small size tumor inside. Using the gradient method, the authors have developed a computer code to create images of the three-dimensional objects in dielectrical properties on microwave frequencies.

Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane

Abstract: A model-based closed-loop control system is presented to regulate hypnosis with the volatile anesthetic isoflurane. Hypnosis is assessed by means of the bispectral index (BIS), a processed parameter derived from the electroencephalogram. Isoflurane is administered through a closed-circuit respiratory system. The model for control was identified on a population of 20 healthy volunteers. It consists of three parts: a model for the respiratory system, a pharmacokinetic model and a pharmacodynamic model to predict BIS at the effect compartment. A cascaded internal model controller is employed. The master controller compares the actual BIS and the reference value set by the anesthesiologist and provides expired isoflurane concentration references to the slave controller. The slave controller maneuvers the fresh gas anesthetic concentration entering the respiratory system. The controller is designed to adapt to different respiratory conditions. Anti-windup measures protect against performance degradation in the event of saturation of the input signal. Fault detection schemes in the controller cope with BIS and expired concentration measurement artifacts. The results of clinical studies on humans are presented.

Automatic differentiation of multichannel EEG signals

Abstract: Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. The authors present a multichannel classification method that uses a "committee" of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform.

Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties

Abstract: Time-frequency analysis is considered for characterizing atrial fibrillation in the surface electrocardiogram (ECG). Variations in fundamental frequency of the fibrillatory waves are tracked by using different time-frequency distributions which are appropriate to short- and long-term variations. The cross Wigner-Ville distribution is found to be particularly useful for short-term analysis due to its ability to handle poor signal-to-noise ratios. In patients with chronic atrial fibrillation, substantial short-term variations exist in fibrillation frequency and variations up to 2.5 Hz can be observed within a few seconds. Although time-frequency analysis is performed independently in each lead, short-term variations in fibrillation frequency often exhibit a similar pattern in the leads V/sub 1/, V/sub 2/ and V/sub 3/. Using different techniques for short- and long-term analysis, it is possible to reliably detect subtle long-term changes in fibrillation frequency, e.g., related to an intervention, which otherwise would have been obscured by spontaneous variations in fibrillation frequency.

A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry [and application to blood flow/pressure data]

Abstract: A linear and nonlinear autoregressive (AR) moving average (MA) (ARMA) identification algorithm is developed for modeling time series data. The new algorithm is based on the concepts of affine geometry in which the salient feature of the algorithm is to remove the linearly dependent ARMA vectors from the pool of candidate ARMA vectors. For noiseless time series data with a priori incorrect model-order selection, computer simulations show that accurate linear and nonlinear ARMA model parameters can be obtained with the new algorithm. Many algorithms, including the fast orthogonal search (FOS) algorithm, are not able to obtain correct parameter estimates in every case, even with noiseless time series data, because their model-order search criteria are suboptimal. For data contaminated with noise, computer simulations show that the new algorithm performs better than the FOS algorithm for MA processes, and similarly to the FOS algorithm for ARMA processes. However, the computational time to obtain the parameter estimates with the new algorithm is faster than with FOS. Application of the new algorithm to experimentally obtained renal blood flow and pressure data show that the new algorithm is reliable in obtaining physiologically understandable transfer function relations between blood pressure and flow signals.

A short-time multifractal approach for arrhythmia detection based on fuzzy neural network

Abstract: The authors have proposed the notion of short-time multifractality and used it to develop a novel approach for arrhythmia detection. Cardiac rhythms are characterized by short-time generalized dimensions (STGDs), and different kinds of arrhythmias are discriminated using a neural network. To advance the accuracy of classification, a new fuzzy Kohonen network, which overcomes the shortcomings of the classical algorithm, is presented. In the authors' paper, the potential of their method for clinical uses and real-time detection was examined using 180 electrocardiogram records [60 atrial fibrillation, 60 ventricular fibrillation, and 60 ventricular tachycardia]. The proposed algorithm has achieved high accuracy (more than 97%) and is computationally fast in detection.