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Showing papers in "IEEE Transactions on Biomedical Engineering in 2003"


Journal ArticleDOI
TL;DR: It is shown that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible and other important characteristics for prosthetic control systems are met.
Abstract: This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.

1,545 citations


Journal ArticleDOI
TL;DR: A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals and may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.
Abstract: A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals. The operator can specify the mean and standard deviation of the heart rate, the morphology of the PQRST cycle, and the power spectrum of the RR tachogram. In particular, both respiratory sinus arrhythmia at the high frequencies (HFs) and Mayer waves at the low frequencies (LFs) together with the LF/HF ratio are incorporated in the model. Much of the beat-to-beat variation in morphology and timing of the human ECG, including QT dispersion and R-peak amplitude modulation are shown to result. This model may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.

1,103 citations


Journal ArticleDOI
TL;DR: The ambulatory system showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides, in both first and second studies.
Abstract: A new method of physical activity monitoring is presented, which is able to detect body postures (sifting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking." 8.2% for sifting, and 1.3% for lying.

778 citations


Journal ArticleDOI
TL;DR: Investigation of the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability concludes that changes in HRV are better quantified by scaling analysis than by spectral analysis.
Abstract: Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.

441 citations


Journal ArticleDOI
TL;DR: An adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known and results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.
Abstract: Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.

437 citations


Journal ArticleDOI
TL;DR: An overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction is presented.
Abstract: Epileptic seizures are manifestations of epilepsy, a serious brain dynamical disorder second only to strokes. Of the world's /spl sim/50 million people with epilepsy, fully 1/3 have seizures that are not controlled by anti-convulsant medication. The field of seizure prediction, in which engineering technologies are used to decode brain signals and search for precursors of impending epileptic seizures, holds great promise to elucidate the dynamical mechanisms underlying the disorder, as well as to enable implantable devices to intervene in time to treat epilepsy. There is currently an explosion of interest in this field in academic centers and medical industry with clinical trials underway to test potential prediction and intervention methodology and devices for Food and Drug Administration (FDA) approval. This invited paper presents an overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction. Broader application of these developments to a variety of systems requiring monitoring, forecasting and control is a natural outgrowth of this field.

418 citations


Journal ArticleDOI
TL;DR: Results show that the normal recordings could be separated from the apnoea recordings with a 100% success rate and a minute-by-minute classification accuracy of over 90% is achievable.
Abstract: A method for the automatic processing of the electrocardiogram (ECG) for the detection of obstructive apnoea is presented. The method screens nighttime single-lead ECG recordings for the presence of major sleep apnoea and provides a minute-by-minute analysis of disordered breathing. A large independently validated database of 70 ECG recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. Thirty-five of these recordings were used for training and 35 retained for independent testing. A wide variety of features based on heartbeat intervals and an ECG-derived respiratory signal were considered. Classifiers based on linear and quadratic discriminants were compared. Feature selection and regularization of classifier parameters were used to optimize classifier performance. Results show that the normal recordings could be separated from the apnoea recordings with a 100% success rate and a minute-by-minute classification accuracy of over 90% is achievable.

418 citations


Journal ArticleDOI
TL;DR: An individualized method for selecting electroencephalogram features and electrode locations for seizure prediction focused on precursors that occur within ten minutes of electrographic seizure onset, as an example of a method for training, testing and validating a seizure prediction system on data from individual patients.
Abstract: Epileptic seizure prediction has steadily evolved from its conception in the 1970s, to proof-of-principle experiments in the late 1980s and 1990s, to its current place as an area of vigorous, clinical and laboratory investigation. As a step toward practical implementation of this technology in humans, we present an individualized method for selecting electroencephalogram (EEG) features and electrode locations for seizure prediction focused on precursors that occur within ten minutes of electrographic seizure onset. This method applies an intelligent genetic search process to EEG signals simultaneously collected from multiple intracranial electrode contacts and multiple quantitative features derived from these signals. The algorithm is trained on a series of baseline and preseizure records and then validated on other, previously unseen data using split sample validation techniques. The performance of this method is demonstrated on multiday recordings obtained from four patients implanted with intracranial electrodes during evaluation for epilepsy surgery. An average probability of prediction (or block sensitivity) of 62.5% was achieved in this group, with an average block false positive (FP) rate of 0.2775 FP predictions/h, corresponding to 90.47% specificity. These findings are presented as an example of a method for training, testing and validating a seizure prediction system on data from individual patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical deployment.

306 citations


Journal ArticleDOI
TL;DR: The exposure of a subject in the far field of radiofrequency sources operating in the 10-900-MHz range has been studied and the thermal model used, which takes into account the thermoregulatory system of the human body, has been validated through a comparison with experimental data.
Abstract: The exposure of a subject in the far field of radiofrequency sources operating in the 10-900-MHz range has been studied. The electromagnetic field inside an anatomical heterogeneous model of the human body has been computed by using the finite-difference time-domain method; the corresponding temperature increase has been evaluated through an explicit finite-difference formulation of the bio-heat equation. The thermal model used, which takes into account the thermoregulatory system of the human body, has been validated through a comparison with experimental data. The results show that the peak specific absorption rate (SAR) as averaged over 10 g has about a 25-fold increase in the trunk and a 50-fold increase in the limbs with respect to the whole body averaged SAR (SAR/sub WB/). The peak SAR as averaged over 1 g, instead, has a 30- to 60-fold increase in the trunk, and up to 135-fold increase in the ankles, with respect to SAR/sub WB/. With reference to temperature increases, at the body resonance frequency of 40 MHz, for the ICNIRP incident power density maximum permissible value, a temperature increase of about 0.7/spl deg/C is obtained in the ankles muscle. The presence of the thermoregulatory system strongly limits temperature elevations, particularly in the body core.

279 citations


Journal ArticleDOI
TL;DR: This paper presents time-domain reconstruction algorithms for the thermoacoustic imaging of biological tissues to planar and cylindrical measurement configurations and generalizes the rigorous reconstruction formulas by employing Green's function technique.
Abstract: In this paper, we present time-domain reconstruction algorithms for the thermoacoustic imaging of biological tissues. The algorithm for a spherical measurement configuration has recently been reported in another paper. Here, we extend the reconstruction algorithms to planar and cylindrical measurement configurations. First, we generalize the rigorous reconstruction formulas by employing Green's function technique. Then, in order to detect small (compared with the measurement geometry) but deeply buried objects, we can simplify the formulas when two practical conditions exist: 1) that the high-frequency components of the thermoacoustic signals contribute more to the spatial resolution than the low-frequency ones, and 2) that the detecting distances between the thermoacoustic sources and the detecting transducers are much greater than the wavelengths of the high-frequency thermoacoustic signals (i.e., those that are useful for imaging). The simplified formulas are computed with temporal back projections and coherent summations over spherical surfaces using certain spatial weighting factors. We refer to these reconstruction formulas as modified back projections. Numerical results are given to illustrate the validity of these algorithms.

277 citations


Journal ArticleDOI
TL;DR: The proposed method for PSD estimation of the HRV by means of the heart timing (HT) signal has one order of magnitude lower error than usual ectopic beats removal strategies in preserving PSD, thus, this strategy better recovers the original clinical indexes of interest.
Abstract: The time-domain signals representing the heart rate variability (HRV) in the presence of an ectopic beat exhibit a sharp transient at the position of the ectopic beat, which corrupts the signal, particularly the power spectral density (PSD) of the HRV. Consequently, there is a need for correction of this type of beat prior to any HRV analysis. This paper deals with the PSD estimation of the HRV by means of the heart timing (HT) signal when ectopic beats are present. These beat occurrence times are modeled from a generalized, continuous time integral pulse frequency modulation model and, from this point of view, a specific method for minimizing the effect of the presence of ectopic beats is presented to work together with the HT signal. By using both, a white noise driven autoregressive model of the HRV signal with artificially introduced ectopic beats and actual heart rate series including ectopic beats, the more usual methods of HRV spectral estimation are compared. Results of the PSD estimation error function of the number of ectopic beats are presented. These results demonstrate that the proposed method has one order of magnitude lower error than usual ectopic beats removal strategies in preserving PSD, thus, this strategy better recovers the original clinical indexes of interest.

Journal ArticleDOI
TL;DR: Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy).
Abstract: Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and simple tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless, very poor indications on fetal pathologies can be inferred from the even automatic CTG analysis methods, which are actually employed. The feeling is that fetal heart rate (FHR) signals and uterine contractions carry much more information on fetal state than is usually extracted by classical analysis methods. In particular, FHR signal contains indications about the neural development of the fetus. However, the methods actually adopted for judging a CTG trace as "abnormal" give weak predictive indications about fetal dangers. We propose a new methodological approach for the CTG monitoring, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy). This preliminary study considers 14 normal fetuses, eight cases of gestational (maternal) diabetes, and 13 intrauterine growth retarded fetuses. A comparison with the traditional time domain analysis is also included. This paper shows that the proposed new parameters are able to separate normal from pathological fetuses. Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies.

Journal ArticleDOI
TL;DR: In this paper, the authors consider epilepsies as dynamical diseases of brain systems since they are manifestations of the property of neuronal networks to display multistable dynamics, and they assume that at least two states of the epileptic brain are possible: the interictal state characterized by a normal, apparently random, steady-state electroencephalography (EEG) ongoing activity, and the seizure state, that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called, in neurology, a seizure.
Abstract: In this overview, we consider epilepsies as dynamical diseases of brain systems since they are manifestations of the property of neuronal networks to display multistable dynamics. To illustrate this concept we may assume that at least two states of the epileptic brain are possible: the interictal state characterized by a normal, apparently random, steady-state electroencephalography (EEG) ongoing activity, and the ictal state, that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called, in neurology, a seizure. The transition between these two states can either occur: 1) as a continuous sequence of phases, like in some cases of mesial temporal lobe epilepsy (MTLE); or 2) as a sudden leap, like in most cases of absence seizures. In the mathematical terminology of nonlinear systems, we can say that in the first case the system's attractor gradually deforms from an interictal to an ictal attractor. The causes for such a deformation can be either endogenous or external. In this type of ictal transition, the seizure possibly may be anticipated in its early, preclinical phases. In the second case, where a sharp critical transition takes place, we can assume that the system has at least two simultaneous interictal and ictal attractors all the time. To which attractor the trajectories converge, depends on the initial conditions and the system's parameters. An essential question in this scenario is how the transition between the normal ongoing and the seizure activity takes place. Such a transition can occur either due to the influence of external or endogenous factors or due to a random perturbation and, thus, it will be unpredictable. These dynamical changes may not be detectable from the analysis of the ongoing EEG, but they may be observable only by measuring the system's response to externally administered stimuli. In the special cases of reflex epilepsy, the leap between the normal ongoing attractor and the ictal attractor is caused by a well-defined external perturbation. Examples from these different scenarios are presented and discussed.

Journal ArticleDOI
TL;DR: This paper shows that cICA can be applied with great success to EM brain signal analysis, with an initial application in automating artifact extraction in EEG and MEG.
Abstract: Independent component analysis (ICA) is a technique which extracts statistically independent components from a set of measured signals. The technique enjoys numerous applications in biomedical signal analysis in the literature, especially in the analysis of electromagnetic (EM) brain signals. Standard implementations of ICA are restrictive mainly due to the square mixing assumption-for signal recordings which have large numbers of channels, the large number of resulting extracted sources makes the subsequent analysis laborious and highly subjective. There are many instances in neurophysiological analysis where there is strong a priori information about the signals being sought; temporally constrained ICA (cICA) can extract signals that are statistically independent, yet which are constrained to be similar to some reference signal which can incorporate such a priori information. We demonstrate this method on a synthetic dataset and on a number of artifactual waveforms identified in multichannel recordings of EEG and MEG. cICA repeatedly converges to the desired component within a few iterations and subjective analysis shows the waveforms to be of the expected morphologies and with realistic spatial distributions. This paper shows that cICA can be applied with great success to EM brain signal analysis, with an initial application in automating artifact extraction in EEG and MEG.

Journal ArticleDOI
TL;DR: The estimation of on-off timing of human skeletal muscles during movement is an important issue in surface electromyography (EMG) signal processing with relevant clinical applications and a novel approach to address this issue is proposed.
Abstract: The estimation of on-off timing of human skeletal muscles during movement is an important issue in surface electromyography (EMG) signal processing with relevant clinical applications. In this paper, a novel approach to address this issue is proposed. The method is based on the identification of single motor unit action potentials from the surface EMG signal with the use of the continuous wavelet transform. A manifestation variable is computed as the maximum of the outputs of a bank of matched filters at different scales. A threshold is applied to the manifestation variable to detect EMG activity. A model, based on the physical structure of the muscle, is used to test the proposed technique on synthetic signals with known features. The resultant bias of the onset estimate is lower than 40 ms and the standard deviation lower than 30 ms in case of additive colored Gaussian noise with signal-to-noise ratio as low as 2 dB. Comparison with previously developed methods was performed, and representative applications to experimental signals are presented. The method is designed for a complete real-time implementation and, thus, may be applied in clinical routine activity.

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the effect of tissue heterogeneity and anisotropy on the electric field and current density distribution induced in the brain during magnetic stimulation and found that tissue heterogeneity can significantly affect the distribution of electric field induced in brain.
Abstract: We investigate the effect of tissue heterogeneity and anisotropy on the electric field and current density distribution induced in the brain during magnetic stimulation. Validation of the finite-element (FE) calculations in a homogeneous isotropic sphere showed that the magnitude of the total electric field can be calculated to within an error of approximately 5% in the region of interest, even in the presence of a significant surface charge contribution. We used a high conductivity inclusion within a sphere of lower conductivity to simulate a lesion due to an infarct. Its effect is to increase the electric field induced in the surrounding low conductivity region. This boost is greatest in the vicinity of interfaces that lie perpendicular to the current flow. For physiological values of the conductivity distribution, it can reach a factor of 1.6 and extend many millimeters from the interface. We also show that anisotropy can significantly alter the electric field and current density distributions. Either heterogeneity or anisotropy can introduce a radial electric field component, not present in a homogeneous isotropic conductor. Heterogeneity and anisotropy are predicted to significantly affect the distribution of the electric field induced in the brain. It is, therefore, expected that anatomically faithful FE models of individual brains which incorporate conductivity tensor data derived from diffusion tensor measurements, will provide a better understanding of the location of possible stimulation sites in the brain.

Journal ArticleDOI
TL;DR: Results demonstrate that /spl rho//sub skull/ is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80, and the correction for geometry errors is essential to obtain realistic estimations.
Abstract: In vivo measurements of equivalent resistivities of skull (/spl rho//sub skull/) and brain (/spl rho//sub brain/) are performed for six subjects using an electric impedance tomography (EIT)-based method and realistic models for the head. The classical boundary element method (BEM) formulation for EIT is very time consuming. However, the application of the Sherman-Morrison formula reduces the computation time by a factor of 5. Using an optimal point distribution in the BEM model to optimize its accuracy, decreasing systematic errors of numerical origin, is important because cost functions are shallow. Results demonstrate that /spl rho//sub skull///spl rho//sub brain/ is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in /spl rho//sub brain/ (average = 301 /spl Omega/ /spl middot/ cm, SD = 13%) and /spl rho//sub skull/ (average = 12230 /spl Omega/ /spl middot/ cm, SD = 18%) is decreased by half, when compared with the results using the sphere model, showing that the correction for geometry errors is essential to obtain realistic estimations. However, a factor of 2.4 may still exist between values of /spl rho//sub skull///spl rho//sub brain/ corresponding to different subjects. Earlier results show the necessity of calibrating /spl rho//sub brain/ and /spl rho//sub skull/ by measuring them in vivo for each subject, in order to decrease errors associated with the electroencephalogram inverse problem. We show that the proposed method is suited to this goal.

Journal ArticleDOI
TL;DR: A novel balanced input ac-coupling network that provides a bias path without any connection to ground, thus resulting in a high CMRR and allows the implementation of high-gain biopotential amplifiers with a reduced number of parts, Thus resulting in low power consumption.
Abstract: AC coupling is essential in biopotential measurements. Electrode offset potentials can be several orders of magnitude larger than the amplitudes of the biological signals of interest, thus limiting the admissible gain of a dc-coupled front end to prevent amplifier saturation. A high-gain input stage needs ac input coupling. This can be achieved by series capacitors, but in order to provide a bias path, grounded resistors are usually included, which degrade the common mode rejection ratio (CMRR). This paper proposes a novel balanced input ac-coupling network that provides a bias path without any connection to ground, thus resulting in a high CMRR. The circuit being passive, it does not limit the differential dc input voltage. Furthermore, differential signals are ac coupled, whereas common-mode voltages are dc coupled, thus allowing the closed-loop control of the dc common mode voltage by means of a driven-right-leg circuit. This makes the circuit compatible with common-mode dc shifting strategies intended for single-supply biopotential amplifiers. The proposed circuit allows the implementation of high-gain biopotential amplifiers with a reduced number of parts, thus resulting in low power consumption. An electrocardiogram amplifier built according to the proposed design achieves a CMRR of 123 dB at 50 Hz.

Journal ArticleDOI
TL;DR: This paper proposes a way to eliminate the requirement of subject rotation by careful mathematical analysis of the MRCDI problem, which needs to measure only one component of the induced magnetic flux density and reconstruct both cross-sectional conductivity and current density images without any subject rotation.
Abstract: Magnetic resonance current density imaging (MRCDI) is to provide current density images of a subject using a magnetic resonance imaging (MRI) scanner with a current injection apparatus. The injection current generates a magnetic field that we can measure from MR phase images. We obtain internal current density images from the measured magnetic flux densities via Ampere's law. However, we must rotate the subject to acquire all of the three components of the induced magnetic flux density. This subject rotation is impractical in clinical MRI scanners when the subject is a human body. In this paper, we propose a way to eliminate the requirement of subject rotation by careful mathematical analysis of the MRCDI problem. In our new MRCDI technique, we need to measure only one component of the induced magnetic flux density and reconstruct both cross-sectional conductivity and current density images without any subject rotation.

Journal ArticleDOI
TL;DR: The goal of this research is to automate Gleason grading of prostate pathological images by calculating energy and entropy features of multiwavelet coefficients of the image and using a k-nearest neighbor classifier to classify each image to appropriate grade (class).
Abstract: Histological grading of pathological images is used to determine the level of malignancy of cancerous tissues. This is a very important task in prostate cancer prognosis, since it is used for treatment planning. If infection of cancer is not rejected by noninvasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. The Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time.

Journal ArticleDOI
TL;DR: A MEA-based stimulation system with novel interface circuit modules, in which preamplifiers and transistor transistor logic-driven solid-state switching devices are integrated, which permits PC-controlled remote switching of each substrate electrode and is a useful tool for studying neuronal signal processing in biological neuronal networks.
Abstract: The capability for multisite stimulation is one of the biggest potential advantages of microelectrode arrays (MEAs). There remain, however, several technical problems which have hindered the development of a practical stimulation system. An important design goal is to allow programmable multisite stimulation, which produces minimal interference with simultaneous extracellular and patch or whole cell clamp recording. Here, we describe a multisite stimulation and recording system with novel interface circuit modules, in which preamplifiers and transistor transistor logic-driven solid-state switching devices are integrated. This integration permits PC-controlled remote switching of each substrate electrode. This allows not only flexible selection of stimulation sites, but also rapid switching of the selected sites between stimulation and recording, within 1.2 ms. This allowed almost continuous monitoring of extracellular signals at all the substrate-embedded electrodes, including those used for stimulation. In addition, the vibration-free solid-state switching made it possible to record whole-cell synaptic currents in one neuron, evoked from multiple sites in the network. We have used this system to visualize spatial propagation patterns of evoked responses in cultured networks of cortical neurons. This MEA-based stimulation system is a useful tool for studying neuronal signal processing in biological neuronal networks, as well as the process of synaptic integration within single neurons.

Journal ArticleDOI
TL;DR: The results suggest that FCNN can generalize better than other NN algorithms and help the user learn better and faster and has the potential of being very efficient in real-time applications.
Abstract: Accurate and computationally efficient means of classifying surface myoelectric signals has been the subject of considerable research effort in recent years. The aim of this paper is to classify myoelectric signals using new fuzzy clustering neural network (NN) architectures to control multifunction prostheses. This paper presents a comparative study of the classification accuracy of myoelectric signals using multilayered perceptron NN using back-propagation, conic section function NN, and new fuzzy clustering NNs (FCNNs). The myoelectric signals considered are used in classifying six upper-limb movements: elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalize better than other NN algorithms and help the user learn better and faster. This method has the potential of being very efficient in real-time applications.

Journal ArticleDOI
TL;DR: Radiation characteristics for sources in the GI tract are presented that should allow for the optimum design of more efficient telemetry systems and are determined using the finite-difference time-domain method with a realistic antenna model on an established fully segmented human body model.
Abstract: The conventional method of diagnosing disorders of the human gastro-intestinal (GI) tract is by sensors embedded in cannulae that are inserted through the anus, mouth, or nose. However, these cannulae cause significant patient discomfort and cannot be used in the small intestine. As a result, there is considerable ongoing work in developing wireless sensors that can be used in the small intestine. The radiation characteristics of sources in the GI tract cannot be readily calculated due to the complexity of the human body and its composite tissues, each with different electrical characteristics. In addition, the compact antennas used are electrically small, making them inefficient radiators. This paper presents radiation characteristics for sources in the GI tract that should allow for the optimum design of more efficient telemetry systems. The characteristics are determined using the finite-difference time-domain method with a realistic antenna model on an established fully segmented human body model. Radiation intensity outside the body was found to have a Gaussian-form relationship with frequency. Maximum radiation occurs between 450 and 900 MHz. The gut region was found generally to inhibit vertically polarized electric fields more than horizontally polarized fields.

Journal ArticleDOI
TL;DR: An action potential detector based on a prudent combination of wavelet coefficients of multiple scales is proposed and its performance for neural signal recording with varying degrees of similarity between signal and noise is demonstrated.
Abstract: We present a method for the detection of action potentials, an essential first step in the analysis of extracellular neural signals. The low signal-to-noise ratio (SNR) and similarity of spectral characteristic between the target signal and background noise are obstacles to solving this problem and, thus, in previous studies on experimental neurophysiology, only action potentials with sufficiently large amplitude have been detected and analyzed. In order to lower the level of SNR required for successful detection, we propose an action potential detector based on a prudent combination of wavelet coefficients of multiple scales and demonstrate its performance for neural signal recording with varying degrees of similarity between signal and noise. The experimental data include recordings from the rat somatosensory cortex, the giant medial nerve of crayfish, and the cutaneous nerve of bullfrog. The proposed method was tested for various SNR values and degrees of spectral similarity. The method was superior to the Teager energy operator and even comparable to or better than the optimal linear detector. A detection ratio higher than 80% at a false alarm ratio lower than 10% was achieved, under an SNR of 2.35 for the rat cortex data where the spectral similarity was very high.

Journal ArticleDOI
TL;DR: In vivo validation of a method for the three-dimensional (3-D) high-resolution modeling of the human spine, rib cage, and pelvis for the study of spinal deformities gives an overall accuracy of 3.3 /spl plusmn/ 3.8 mm, making it an adequate tool for clinical studies and mechanical analysis purposes.
Abstract: This paper presents an in vivo validation of a method for the three-dimensional (3-D) high-resolution modeling of the human spine, rib cage, and pelvis for the study of spinal deformities. The method uses an adaptation of a standard close-range photogrammetry method called direct linear transformation to reconstruct the 3-D coordinates of anatomical landmarks from three radiographic images of the subject's trunk. It then deforms in 3-D 1-mm-resolution anatomical primitives (reference bones) obtained by serial computed tomography-scan reconstruction of a dry specimen. The free-form deformation is calculated using dual kriging equations. In vivo validation of this method on 40 scoliotic vertebrae gives an overall accuracy of 3.3 /spl plusmn/ 3.8 mm, making it an adequate tool for clinical studies and mechanical analysis purposes.

Journal ArticleDOI
TL;DR: A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure.
Abstract: Radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision-making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.

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TL;DR: A significant decrease of synchrony in the focal area several minutes before seizures in the frequency band of 10-25 Hz mainly is reported, which may open new perspectives on the mechanisms of seizure emergence as well as the organization of neocortical epileptogenic networks.
Abstract: The mechanisms underlying the transition of brain activity toward epileptic seizures remain unclear. Based on nonlinear analysis of both intracranial and scalp electroencephalographic (EEG) recordings, different research groups have recently reported dynamical smooth changes in epileptic brain activity several minutes before seizure onset. Such preictal states have been detected in populations of patients with mesial temporal lobe epilepsy (MTLE) and, more recently, with different neocortical partial epilepsies (NPEs). In this paper, we are particularly interested in the spatio-temporal organization of epileptogenic networks prior to seizures in neocortical epilepsies. For this, we characterize the network of two patients with NPE by means of two nonlinear measures of interdependencies. Since the synchronization of neuronal activity is an essential feature of the generation and propagation of epileptic activity, we have analyzed changes in phase synchrony between EEG time series. In order to compare the phase and amplitude dynamics, we have also studied the degree of association between pairs of signals by means of a nonlinear correlation coefficient. Recent findings have suggested changes prior to seizures in a wideband frequency range. Instead, for the examples of this study, we report a significant decrease of synchrony in the focal area several minutes before seizures (/spl Gt/30 min in both patients) in the frequency band of 10-25 Hz mainly. Furthermore, the spatio-temporal organization of this preictal activity seems to be specifically related to this frequency band. Measures of both amplitude and phase coupling yielded similar results in narrow-band analysis. These results may open new perspectives on the mechanisms of seizure emergence as well as the organization of neocortical epileptogenic networks. The possibility of forecasting the onset of seizures has important implications for a better understanding, diagnosis and a potential treatment of the epilepsy.

Journal ArticleDOI
TL;DR: Radiofrequency (RF) ablation is a minimally invasive method for treatment of primary and metastatic liver tumors that employs an internally cooled 17-gauge needle probe, which cools tissue close to the probe resulting in larger lesions.
Abstract: Radiofrequency (RF) ablation is a minimally invasive method for treatment of primary and metastatic liver tumors. One of the currently commercially available devices employs an internally cooled 17-gauge needle probe. Within the probe, cool water is circulated during ablation, which cools tissue close to the probe resulting in larger lesions. We evaluated the effect of different cooling water temperatures on lesion size. We created a finite-element method model, simulated 12 min impedance-controlled ablation and determined temperature distribution for three water temperatures. Lesion diameters in the model were 33.8, 33.4, and 32.8 mm for water temperatures of 5/spl deg/C, 15/spl deg/C, and 25/spl deg/C, respectively. We solved a simplified model geometry analytically and present dependence of lesion diameter on cooling temperature. We further performed ex vivo experiments in fresh bovine liver. We created four lesions for each water temperature, with the same water temperatures as used in the finite-element method (FEM) model. Average lesion diameters were 28.3, 30, and 29.5 mm for water temperatures of 5/spl deg/C, 15/spl deg/C, and 25/spl deg/C, respectively. Water temperature did not have a significant effect on lesion size in the ex vivo experiments (p=0.76), the FEM model, and the analytical solution.

Journal ArticleDOI
TL;DR: This study finds that the current densities and electric fields in the ECT case are stronger and deeper penetrating than the corresponding TMS quantities but both methods show biologically interesting current levels deep inside the brain.
Abstract: A comparative, computational study of the modeling of transcranial magnetic stimulation (TMS) and electroconvulsive therapy (ECT) is presented using a human head model. The magnetic fields from a typical TMS coil of figure-eight type is modeled using the Biot-Savart law. The TMS coil is placed in a position used clinically for treatment of depression. Induced current densities and electric field distributions are calculated in the model using the impedance method. The calculations are made using driving currents and wave forms typical in the clinical setting. The obtained results are compared and contrasted with the corresponding ECT results. In the ECT case, a uniform current density is injected on one side of the head and extracted from the equal area on the opposite side of the head. The area of the injected currents corresponds to the electrode placement used in the clinic. The currents and electric fields, thus, produced within the model are computed using the same three-dimensional impedance method as used for the TMS case. The ECT calculations are made using currents and wave forms typical in the clinic. The electrical tissue properties are obtained from a 4-Cole-Cole model. The numerical results obtained are shown on a two-dimensional cross section of the model. In this study, we find that the current densities and electric fields in the ECT case are stronger and deeper penetrating than the corresponding TMS quantities but both methods show biologically interesting current levels deep inside the brain.

Journal ArticleDOI
TL;DR: The fluorescence excitation-emission wavelengths identified as being diagnostic from the PCA-SVM algorithm suggest that the important fluorophores for breast cancer diagnosis are most likely tryptophan, NAD(P)H and flavoproteins.
Abstract: Nonmalignant (n = 36) and malignant (n = 20) tissue samples were obtained from breast cancer and breast reduction surgeries. These tissues were characterized using multiple excitation wavelength fluorescence spectroscopy and diffuse reflectance spectroscopy in the ultraviolet-visible wavelength range, immediately after excision. Spectra were then analyzed using principal component analysis (PCA) as a data reduction technique. PCA was performed on each fluorescence spectrum, as well as on the diffuse reflectance spectrum individually, to establish a set of principal components for each spectrum. A Wilcoxon rank-sum test was used to determine which principal components show statistically significant differences between malignant and nonmalignant tissues. Finally, a support vector machine (SVM) algorithm was utilized to classify the samples based on the diagnostically useful principal components. Cross-validation of this nonparametric algorithm was carried out to determine its classification accuracy in an unbiased manner. Multiexcitation fluorescence spectroscopy was successful in discriminating malignant and nonmalignant tissues, with a sensitivity and specificity of 70% and 92%, respectively. The sensitivity (30%) and specificity (78%) of diffuse reflectance spectroscopy alone was significantly lower. Combining fluorescence and diffuse reflectance spectra did not improve the classification accuracy of an algorithm based on fluorescence spectra alone. The fluorescence excitation-emission wavelengths identified as being diagnostic from the PCA-SVM algorithm suggest that the important fluorophores for breast cancer diagnosis are most likely tryptophan, NAD(P)H and flavoproteins.