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PubMed2002

Wavelet de-noising of laser Doppler reactive hyperemia signals to diagnose peripheral arterial occlusive diseases.

In order to improve peripheral arterial occlusive diseases (PAOD) diagnoses, five de-noising algorithms based on a multiresolution analysis computed with wavelets are applied on reactive hyperemia signals obtained with the laser Doppler flowmetry technique. Results are presented on recordings acquired on patients suffering from PAOD and on healthy subjects.

Arterial Occlusive Diseases↗
PubMed2002

Estimation of kinetic parameters without input functions: analysis of three methods for multichannel blind identification.

Compartment modeling of dynamic medical image data implies that the concentration of the tracer over time in a particular region of the organ of interest is well modeled as a convolution of the tissue response with the tracer concentration in the blood stream. The tissue response is different for different tissues while the blood input is assumed to be the same for different tissues. The kinetic parameters characterizing the tissue responses can be estimated by multichannel blind identification methods. These algorithms use the simultaneous measurements of concentration in separate regions of the organ; if the regions have different responses, the measurement of the blood input function may not be required. Three blind identification algorithms are analyzed here to assess their utility in medical imaging: eigenvector-based algorithm for multichannel blind deconvolution; cross relations; and iterative quadratic maximum-likelihood (IQML). Comparisons of accuracy with conventional (not blind) identification techniques where the blood input is known are made as well. Tissue responses corresponding to a physiological two-compartment model are primarily considered. The statistical accuracies of estimation for the three methods are evaluated and compared for multiple parameter sets. The results show that IQML gives more accurate estimates than the other two blind identification methods.

Algorithms↗
PubMed2002

Techniques to improve the accuracy and to reduce the variance in noise power spectrum measurement.

Several techniques to increase the accuracy and to reduce the variance of the noise power spectrum (NPS) measurement for digital X-ray imaging systems are investigated. These techniques include: 1) averaging the outputs from subblocks of the entire image; 2) averaging the two-dimensional NPS data along a circular route centered on the origin of spectral domain; and 3) masking a window function on each subblock before Fourier transforms. Techniques 1) and 2) are used mainly to reduce the variance of the NPS measurement. Technique 3) serves to improve the accuracy of the final result. Experiments with two different charge-coupled device-based X-ray imaging systems demonstrated that the precision and accuracy of the NPS measurement could be significantly improved using these techniques. The impact of the image partition for averaging is discussed and the corresponding NPS estimations are presented for the number of subblocks ranging from 4 to 64. The effect of masking on the NPS is also studied using four different window functions.

Models, Statistical↗
PubMed2002

Estimation of plasma insulin from plasma glucose.

Two plasma insulin estimators are derived and evaluated in this paper. The estimators use continuously monitored plasma glucose, and they are based on a simple mathematical model of glucose-insulin metabolism. The first estimator (Estimator 1) was obtained from the glucose subsystem of the simplified model, which includes plasma glucose and interstitial insulin compartments. The second estimator (Estimator 2) was developed from the insulin kinetics subsystem of the simplified model. The performances of the estimators were compared using computer simulations and data from clinical intravenous glucose tolerance test procedures and from animal experiments.

Animals↗
PubMed2002

Instantaneous parameter estimation in cardiovascular time series by harmonic and time-frequency analysis.

Time-frequency distributions, such as smoothed pseudo Wigner-Ville distribution (SPWVD), complex demodulation (CDM), and provide useful time-varying spectral parameter estimators. However, each of these methods has limitations that a joint utilization could largely reduce, due to their interesting complementary features. The aim of this paper is to validate the joint SPWVD-CDM method on synthetic and real cardiovascular time series with normal and reduced variability such as in autonomic blockade or autonomic deficiency. We propose two indexes related to the noise present in the signal and to the dispersion of the power spectrum in order to validate instantaneous parameter estimation. In the low-frequency band, the interpretation of the instantaneous frequency and phase of cardiovascular time-series should be discarded in many real-life situations. Conversely, in the high frequency band, under paced breathing, the reliability of the instantaneous parameters is demonstrated even in conditions of reduced cardiovascular variability.

Algorithms↗
PubMed2003

An improved morphological approach to background normalization of ECG signals.

This paper describes an improved morphological approach to remove baseline wander from neonatal electrocardiogram (ECG) signals, with particular emphasis on preserving the ST segment of the original signal. The algorithm consists of two stages of morphological processing. First, the QRS complex and impulsive noise component due to skeletal muscle contractions etc., are detected and removed from the input signal. Second, the corrected QT interval (QTc) and RR interval are used to determine a structuring element. With this structuring element, the same morphological operation as in the first stage is then applied to the QRS-removed signal to obtain and remove the baseline wander. The performance of the algorithm is evaluated with simulated and real ECGs. Compared with an existing morphological method, there is a substantial improvement, especially in reducing distortion of the baseline waveform within the PR and QT intervals.

Algorithms↗
PubMed2003

Noise reduction in directional signals using multiple morse wavelets illustrated on quadrature Doppler ultrasound.

The use of multiple complex-valued Morse wavelets for the scalogram study of signals which are unidirectional at any time, but are bidirectional overall is considered. These wavelets are well-suited to identifying the forward and reverse components. Scalogram averaging which is possible due to the multiplicity of the complex-valued wavelets leads to a scalogram with reduced noise. Information from positive and negative scales can then be used to estimate a final "cleaned" scalogram. Quadrature Doppler ultrasound blood flow in the femoral artery is taken as an example to clearly illustrate the noise reduction.

Blood Flow Velocity↗
PubMed2003

A fast and reliable technique for muscle activity detection from surface EMG signals.

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.

Action Potentials↗
PubMed2003

Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio.

Neural spike sorting is an indispensable step in the analysis of multiunit extracellular neural signal recording. The applicability of spike sorting systems has been limited, mainly to the recording of sufficiently high signal-to-noise ratios, or to the cases where supervised classification can be utilized. We present a novel unsupervised method that shows satisfactory performance even under high background noise. The system consists of an efficient spike detector, a feature extractor that utilizes projection pursuit based on negentropy maximization (Huber, 1985 and Hyvarinen et al, 1999), and an unsupervised classifier based on probability density modeling using mixture of Gaussians (Jain et al., 2000). Our classifier is based on the mixture model with a roughly approximated number of Gaussians and subsequent mode-seeking. It does not require accurate estimation of the number of units present in the recording and, thus, is better suited for use in fully automated systems. The feature extraction stage leads to better performance than those utilizing principal component analysis and two nonlinear mappings for the recordings from the somatosensory cortex of rat and the abdominal ganglion of Aplysia. The classification method yielded correct classification ratio as high as 95%, for data where it was only 66% when a kappa-means-type algorithm was used for the classification stage.

Action Potentials↗
PubMed2003

Efficient estimation of a time-varying dimension parameter and its application to EEG analysis.

This paper considers the problem of estimating the dimension of nonstationary electroencephalogram (EEG) signals and describes the implementation of an efficient algorithm to calculate a time-varying dimension estimate. The algorithm allows the practical calculation of a dimension estimate and its statistical significance over large data sets with a high temporal resolution. The method is applied to EEG recordings from patients with temporal lobe epilepsy and in one case the results of the analysis are compared with those obtained from an existing method of computing the correlation density.

Algorithms↗
PubMed2003

Channel-consistent forewarning of epileptic events from scalp EEG.

Phase-space dissimilarity measures (PSDM) have been recently proposed to provide forewarning of impending epileptic events from scalp electroencephalographic (EEG) for eventual ambulatory settings. Despite high noise in scalp EEG, PSDM yield consistently superior performance over traditional nonlinear indicators, such as Kolmogorov entropy, Lyapunov exponents, and correlation dimension. However, blind application of PSDM may result in channel inconsistency, whereby multiple datasets from the same patient yield conflicting forewarning indications in the same channel. This paper presents a first attempt to solve this problem.

Adolescent↗
PubMed2003

ECG-based detection of body position changes in ischemia monitoring.

The purpose of this paper is to analyze and detect changes in body position (BPC) during electrocardiogram (ECG) recording. These changes are often manifested as shifts in the electrical axis and may be misclassified as ischemic changes during ambulatory monitoring. We investigate two ECG signal processing methods for detecting BPCs. Different schemes for feature extraction are used (spatial and scalar), while preprocessing, trend postprocessing and detection are identical. The spatial approach is based on VCG loop rotation angles and the scalar approach is based on the Karhunen-Loève transform (KLT) coefficients. The methods are evaluated on two different databases: a database with annotated BPCs and the STAFF III database with recordings from rest and during angioplasty-induced ischemia but not including BPCs. The angle-based detector results in performance values of detection probability PD = 95%, false alarm probability PF = 3% in the BPC database and false alarm rate in the STAFF III database in control ECGs during rest RF(c) = 2 h(-1) (episodes per hour) and in ischemia recordings during angioplasty RF(a) = 7 h(-1), whereas the KLT-based detector produces values of PD = 89%, PF = 3%, RF(c) = 4 h(-1), and RF(a) = 11 h(-1), respectively. Including information on noise level in the detection process to reduce the number of false alarms, performance values of PD approximately equal to 90%, PF approximately equal to 1%, RF(c) approximately equal to 1 h(-1) and RF(a) approximately equal to 2 h(-1) are obtained with both methods. It is concluded that reliable detection of BPCs may be achieved using the ECG signal and should work in parallel to ischemia detectors.

Artifacts↗
PubMed2004

Robust algorithm for estimation of time-varying transfer functions.

We introduce a new method to estimate reliable time-varying (TV) transfer functions (TFs) and TV impulse response functions. The method is based on TV autoregressive moving average models in which the TV parameters are accurately obtained using the optimal parameter search method which we have previously developed. The new method is more accurate than the recursive least-squares (RLS), and remains robust even in the case of significant noise contamination. Furthermore, the new method is able to track dynamics that change abruptly, which is certainly a deficiency of the RLS. Application of the new method to renal blood pressure and flow revealed that hypertensive rats undergo more complex and TV autoregulation in maintaining stable blood flow than do normotensive rats. This observation has not been previously revealed using time-invariant TF analyses. The newly developed approach may promote the broader use of TV system identification in studies of physiological systems and makes linear and nonlinear TV modeling possible in certain cases previously thought intractable.

Algorithms↗
PubMed2004

An interference cancellation algorithm for noninvasive extraction of transabdominal fetal electroencephalogram (TaFEEG).

The electroencephalogram is a noninvasive method of demonstrating cerebral function. The fetal electroencephalogram (FEEG) contains important information regarding the status of a fetus. It is believed that disorganization of normal FEEG development may help detect the onset of cerebral palsy and mental retardation syndromes. Unfortunately, noninvasive methods of monitoring FEEG are not currently available. Noninvasively obtained abdominal surface electrical recordings include FEEG components, but are dominated by large interfering components, and, thus, have very low signal to noise ratio. In this paper, we propose a multistep extraction procedure to separate the four main components in transabdominal recordings: 1) maternal ECG; 2) FECG; and 3) FEEG signals as well as 4) interfering baseline wander. The algorithm is tested on simulated and real transabdominal recordings. This study shows that the proposed method successfully extracts the desired FEEG signal.

Abdomen↗
PubMed2004

Dynamical resetting of the human brain at epileptic seizures: application of nonlinear dynamics and global optimization techniques.

Epileptic seizures occur intermittently as a result of complex dynamical interactions among many regions of the brain. By applying signal processing techniques from the theory of nonlinear dynamics and global optimization to the analysis of long-term (3.6 to 12 days) continuous multichannel electroencephalographic recordings from four epileptic patients, we present evidence that epileptic seizures appear to serve as dynamical resetting mechanisms of the brain, that is the dynamically entrained brain areas before seizures disentrain faster and more frequently (p < 0.05) at epileptic seizures than any other periods. We expect these results to shed light into the mechanisms of epileptogenesis, seizure intervention and control, as well as into investigations of intermittent spatiotemporal state transitions in other complex biological and physical systems.

Adaptation, Physiological↗
PubMed2004

Estimation of nonstationary EEG with Kalman smoother approach: an application to event-related synchronization (ERS).

An adaptive spectrum estimation method for nonstationary electroencephalogram by means of time-varying autoregressive moving average modeling is presented. The time-varying parameter estimation problem is solved by Kalman filtering along with a fixed-interval smoothing procedure. Kalman filter is an optimal filter in the mean square sense and it is a generalization of other adaptive filters such as recursive least squares or least mean square. Furthermore, by using the smoother the unavoidable tracking lag of adaptive filters can be avoided. Due to the properties of Kalman filter and benefits of the smoothing the time-frequency resolution of the presented Kalman smoother spectra is extremely high. The presented approach is applied to estimation of event-related synchronization/desynchronization (ERS/ERD) dynamics of occipital alpha rhythm measured from three healthy subjects. With the Kalman smoother approach detailed spectral information can be extracted from single ERS/ERD samples.

Algorithms↗
PubMed2004

Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation.

This paper proposes the use of variational Kalman filtering as an inference technique for adaptive classification in a brain computer interface (BCI). The proposed algorithm translates electroencephalogram segments adaptively into probabilities of cognitive states. It, thus, allows for nonstationarities in the joint process over cognitive state and generated EEG which may occur during a consecutive number of trials. Nonstationarities may have technical reasons (e.g., changes in impedance between scalp and electrodes) or be caused by learning effects in subjects. We compare the performance of the proposed method against an equivalent static classifier by estimating the generalization accuracy and the bit rate of the BCI. Using data from two studies with healthy subjects, we conclude that adaptive classification significantly improves BCI performance. Averaging over all subjects that participated in the respective study, we obtain, depending on the cognitive task pairing, an increase both in generalization accuracy and bit rate of up to 8%. We may, thus, conclude that adaptive inference can play a significant contribution in the quest of increasing bit rates and robustness of current BCI technology. This is especially true since the proposed algorithm can be applied in real time.

Algorithms↗
PubMed2004

Modeling and decoding motor cortical activity using a switching Kalman filter.

We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.

Action Potentials↗