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PubMed2006

A linear modulation-based stochastic resonance algorithm applied to the detection of weak chromatographic peaks.

A simple stochastic resonance algorithm based on linear modulation was developed to amplify and detect weak chromatographic peaks. The output chromatographic peak is often distorted when using the traditional stochastic resonance algorithm due to the presence of high levels of noise. In the new algorithm, a linear modulated double-well potential is introduced to correct for the distortion of the output peak. Method parameter selection is convenient and intuitive for linear modulation. In order to achieve a better signal-to-noise ratio for the output signal, the performance of two-layer stochastic resonance was evaluated by comparing it with wavelet-based stochastic resonance. The proposed algorithm was applied to the quantitative analysis of dimethyl sulfide and the determination of chloramphenicol residues in milk, and the good linearity of the method demonstrated that it is an effective tool for detecting weak chromatographic peaks.

Algorithms↗

PubMed1985

Intracortical generators of the flash VEP in monkeys.

Flash visual evoked potentials (VEPs) in unanesthetized monkeys were recorded from the cortical surface and from closely spaced intracortical sites together with associated multiple unit activity (MUA). The VEP depth profiles were subjected to current source density (CSD) analysis to delineate the laminar pattern of transmembrane current flows manifested by extracellular source and sinks. The initial surface recorded components (P15 and P18) were generated subcortically within the thalamocortical radiations. The distribution of current sources and sinks associated with two subsequent surface negative components. N24 and N40. demonstrates their generation within laminae IVA and IVCb respectively, both parvocellular thalamorecipient layers. Oscillatory potentials resembling those seen in human VEPs are observed riding on N40; analysis of MUA in conjunction with sources and sinks coincident with these wavelets provides evidence that they derive from both thalamocortical and cortical activity. MUA in the 20-60 msec range shows phasic increases throughout lamina IV, which are maximum in amplitude within lamina IVA. This increased firing is concurrent with the sinks observed within the parvocellular thalamorecipient sublaminae IVCb and IVA. A subsequent component, P65, coincident with a decrease in MUA to below the spontaneous level co-located with a lamina IVCb current source, probably arises from intracortically generated inhibitory activity within IVCb. The next VEP component, a surface negative potential at 95 msec, is coincident with current sources and sinks in lamina III, and is consistent with stellate cell input to supragranular elements. VEP components after N95 are not associated with either MUA or CSD activity and are probably generated in extrastriate cortex. Human counterparts of the simian VEP are proposed.

Animals↗

PubMed2005

EEG nonstationarity during intracranially recorded seizures: statistical and dynamical analysis.

OBJECTIVE: The investigation of nonstationarity in complex, multivariable signals, such as electroencephalographic (EEG) recordings, requires the application of different and novel approaches to analysis. In this study, we have divided the EEG recordings during epileptic seizures into sequential stages using spectral and statistical analysis, and have as well reconstructed discrete-time models (maps) that reflect dynamical (deterministic) properties of the EEG voltage time series. METHODS: Intracranial human EEG recordings with epileptic seizures from three different subjects with medically intractable temporal lobe epilepsy were studied. The methods of statistical (power spectra, wavelet spectra, and one-dimensional probability distribution functions) and dynamical (comparison of dynamical models) nonstationarity analysis were applied. RESULTS: Dynamical nonstationarity analysis revealed more detailed inner structure within the seizures than the statistical analysis. Three or four stages with different dynamics are typically present within seizures. The difference between interictal activity and seizure events was also more evident through dynamical analysis. CONCLUSIONS: Nonstationarity analysis can reveal temporal structure within an epileptic seizure, which could further understanding of how seizures evolve. The method could also be used for identification of seizure onset. SIGNIFICANCE: Our approach reveals new information about the temporal structure of seizures, which is inaccessible using conventional methods.

Electroencephalography↗

PubMed1998

Anesthesia control using midlatency auditory evoked potentials.

This paper shows the development of a system to control inhalation anesthetic concentration delivered to a patient based upon that patient's midlatency auditory evoked potentials (MLAEP's). It was developed and tested in dogs by determining response to the supramaximal stimulus of tail clamping. Prior to tail clamp, the MLAEP was recorded along with inhalational anesthetic concentration and classified as responders or nonresponders as determined by tail clamping. This was performed at a number of different anesthetic levels to obtain a data training set. The MLAEP's were compacted by means of discrete time wavelet transform (DTWT), and together with anesthetic concentration value, a stepwise discriminant analysis (SDA) was performed to determine those features which could separate responders from nonresponders. It was determined that only three features were necessary for this recognition. These features were then used to train a four-layer artificial neural network (ANN) to separate the responders from nonresponders. The network was tested using a separate set of data, resulting in a 93% recognition rate in the anesthetic transition zone between responders and nonresponders, and 100% recognition rate outside this zone. The anesthetic controller used this ANN combined with fuzzy logic and rule-based control. A set of ten animal experiments were performed to test the robustness of this controller. Acceptable clinical performance was obtained, showing the feasibility of this approach.

Anesthesia↗

PubMed2005

A new class of wavelet networks for nonlinear system identification.

A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions.

Algorithms↗

PubMed1997

Real and simulated clustered microcalcifications in digital mammograms. ROC study of observer performance.

We have developed a model to simulate clustered microcalcifications on digital mammograms. Wavelet transform techniques were used to detect real clustered microcalcifications. A feature analysis process was applied to automatically extract the features describing the individual simulated microcalcifications and clusters from the values of the real clustered microcalcifications present in the mammogram. Subsequently, a database of simulated and real clustered microcalcifications was created. Clusters of microcalcifications from this database were tested for indistinguishability from real ones. Two radiologists and one physicist were asked to indicate whether the microcalcifications were either real or simulated. The responses of the readers were evaluated with a ROC analysis and the area under the curve was calculated. The average ROC area was 0.54 +/- 0.03, indicating there was no statistical difference between real and simulated clustered microcalcifications. The method allows for the creations of simulated clustered microcalcifications that are virtually indistinguishable from real microcalcifications in digital mammograms and could be used to evaluate different image processing techniques.

Biophysical Phenomena↗

PubMed2001

[Maximal entropy principle wavelet denoising].

In the filed of wavelet denoising, an essential problem is how to determine the cutting threshold of wavelet coefficients that divides the coefficients corresponding to signal and noise respectively. The wavelet denoising method discussed here determines this threshold by using the maximal entropy principle (MEP) of information theory. From the basic principle of probability theory, it can be deduced that the detailed wavelet coefficients sequence of an arbitrary distributed random noise sequence satisfies a normal distribution. Based on this conclusion, an optimal threshold is determined using MEP. Such that the coefficients whose absolute values are less than the threshold satisfies a normal probabilistic distribution. This threshold is an optimal value that distinguishes the wavelet coefficients of signal and noise in view of statistics. The simulation analysis using spectral data and the comparison with other methods showed that this method provides a best improvement of signal-to-noise ratio, and its performance is least sensitive to the change of signal-to-noise ratio.

Algorithms↗

PubMed2006

The characteristics of postcountershock pulseless electrical activity may indicate the outcome of CPR.

OBJECTIVES: When ventricular fibrillation is cardioverted to pulseless electrical activity (PEA), PEA has been regarded as a non-resuscitatable rhythm. Yet, recent reports and our earlier observations suggested otherwise. We therefore investigated outcomes after postcountershock PEA, and aimed to develop a scoring system for outcome classification at the onset of initial postcountershock PEA. METHODS: Data from 215 domestic pigs were retrospectively reviewed. VF was induced and untreated for 7 min. Defibrillation was attempted with up to three 150 J biphasic shocks. Failing to restore spontaneous circulation (ROSC), 1 min of CPR preceded subsequent sequences of shocks until animals were resuscitated or for a total of 15 min. Fifty-nine instances of PEA followed defibrillation, including 29 animals that attained ROSC. RESULTS: ROSC animals required a shorter interval between the first shock and the initial postcountershock PEA, fewer shocks prior to onset of initial postcountershock PEA, demonstrated a greater VF wavelet amplitude prior to onset of initial PEA, smaller QRS intervals, and higher heart rates. Using Fisher's linear discriminant analysis, 79.3% of the ROSC and 63.3% of non-ROSC cases were predicted correctly. A total of 71.2% of all cases were classified correctly. CONCLUSIONS: Animals in which postcountershock PEA was converted to ROSC required shorter intervals from first shock to initial postcountershock PEA, fewer shocks prior to onset of initial postcountershock PEA, had greater VF wavelet amplitude prior to initial postcountershock PEA, small QRS intervals, and higher heart rates. Fisher's discriminant analysis is helpful in predicting the likelihood of ROSC for an individual animal presenting with postcountershock PEA.

Animals↗

PubMed1999

Tight frames of k-plane ridgelets and the problem of representing objects that are smooth away from d-dimensional singularities in Rn.

For each pair (n, k) with 1 </= k < n, we construct a tight frame (rholambda : lambda in Lambda) for L2 (Rn), which we call a frame of k-plane ridgelets. The intent is to efficiently represent functions that are smooth away from singularities along k-planes in Rn. We also develop tools to help decide whether k-plane ridgelets provide the desired efficient representation. We first construct a wavelet-like tight frame on the X-ray bundle chin,k-the fiber bundle having the Grassman manifold Gn,k of k-planes in Rn for base space, and for fibers the orthocomplements of those planes. This wavelet-like tight frame is the pushout to chin,k, via the smooth local coordinates of Gn,k, of an orthonormal basis of tensor Meyer wavelets on Euclidean space Rk(n-k) x Rn-k. We then use the X-ray isometry [Solmon, D. C. (1976) J. Math. Anal. Appl. 56, 61-83] to map this tight frame isometrically to a tight frame for L2(Rn)-the k-plane ridgelets. This construction makes analysis of a function f in L2(Rn) by k-plane ridgelets identical to the analysis of the k-plane X-ray transform of f by an appropriate wavelet-like system for chin,k. As wavelets are typically effective at representing point singularities, it may be expected that these new systems will be effective at representing objects whose k-plane X-ray transform has a point singularity. Objects with discontinuities across hyperplanes are of this form, for k = n - 1.

Journal Article↗

PubMed2005

Comparison of trend detection algorithms in the analysis of physiological time-series data.

This paper presents a comparative performance analysis of various trend detection methods developed using fuzzy logic, statistical, regression, and wavelet techniques. The main contribution of this paper is the introduction of a new method that uses noise rejection fuzzy clustering to enhance the performance of trend detection methodologies. Furthermore, another contribution of this work is a comparative investigation that produced systematic guidelines for the selection of a proper trend detection method for different application requirements. Examples of representative physiological variables considered in this paper to examine the trend detection algorithms are: 1) blood pressure signals (diastolic and systolic); and 2) heartbeat rate based on RR intervals of electrocardiography signal. Furthermore, synthetic physiological data intentionally contaminated with various types of real-life noise has been generated and used to test the performance of trend detection methods and develop noise-insensitive trend-detection algorithms.

Algorithms↗

PubMed1999

Predicting movement during anaesthesia by complexity analysis of electroencephalograms.

A new approach to predicting movement during anaesthesia by using complexity analysis of electroencephalograms (EEG) signals is presented. The raw EEG signal is first decomposed into six consecutive different scaling components by wavelet transform on the basis of its self-similarity. The Lempel-Ziv complexity measures C(n) are extracted from the raw EEG and its corresponding components by complexity analysis. Prediction of movement during anaesthesia is then made by a four-layer artificial neural network (ANN) using the C(n)s. The combination of these three different approaches enables the system to address the non-analytical, non-stationary, non-linear and dynamical properties of the EEG. From 20 dog experiments, 109 distinct EEG recordings are collected under isoflurane anaesthesia. Testing the ANN using the 'drop one dog' method, the performance obtained for the system in detecting movement is: sensitivity 88%, specificity 97% and accuracy 92%. Comparisons with other methods, such as spectral edge frequency, median frequency and principal component analysis, show that the proposed system has a certain advantage. This new method is computationally fast and well suited for realtime clinical implementation.

Anesthesia↗

PubMed2003

Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method.

There are many data mining techniques for processing and general learning of multivariate data. However, we believe the wavelet transformation and latent variable projection method are particularly useful for spectroscopic and chromatographic data. Projection based methods are designed to handle hugely multivariate nature of such data effectively. For the actual analysis of the data we have used latent variable projection methods such as principal component analysis (PCA) and partial least squares projection to latent structures based discriminant analysis (PLS-DA) to analyze the raw data presented to the participants of the First Duke Proteomics Data Mining Conference. PCA was used to solve problem #1 (clustering problem) and the PLS-DA was used to solve problem #2 (classification problem). The idea of internal and external cross-validation was used to validate the model obtained from the classification analysis. The simple two-component PLS-DA model obtained from the analysis performed well. The model has completely separated the two groups from all the data. The same model applied on two-thirds of the data showed good performance by external validation with independent test set of remaining 13 specimens obtained by setting aside the spectra of every third specimen (accuracy of 85%).

Artificial Intelligence↗

PubMed2005

Diagnosing aortic valve stenosis by parameter extraction of heart sound signals.

The objective of this study was to develop an automatic signal analysis system for heart sound diagnosis. This should support the general practitioner in discovering aortic valve stenoses at an early stage to avoid or decrease the number of surgical interventions. The applied analysis method is based on classification of heart sound signals utilising parameter extraction. From the wavelet decomposition of a representative heart cycle as well as from the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT) spectra new time series were derived. In several segments, parameters were extracted and analysed. In addition, features of the Fast Fourier Transform (FFT) of the raw signal were examined. In this study, 206 patients were enrolled, 159 with no heart valve disease or any other heart valve disease but aortic valve stenosis and 47 suffering from aortic valve stenosis in a mild, moderate or severe stage. To separate the groups, a linear discriminant function analysis was applied leading to a reduced parameter set. The introduced two classification stage (CS) system for automatic detection of aortic valve stenoses achieves a high sensitivity of 100% for moderate and severe aortic valve stenosis and a sensitivity of 75% for mild aortic valve stenosis. A specificity of 93.7% for patients without aortic valve stenosis is provided. The developed method is robust, cost effective and easy to use, and could, therefore, be a suitable method to diagnose aortic valve stenosis by general practitioners.

Aortic Valve Stenosis↗

PubMed2004

A comparative evaluation of wavelet-based methods for hypothesis testing of brain activation maps.

Wavelet-based methods for hypothesis testing are described and their potential for activation mapping of human functional magnetic resonance imaging (fMRI) data is investigated. In this approach, we emphasise convergence between methods of wavelet thresholding or shrinkage and the problem of hypothesis testing in both classical and Bayesian contexts. Specifically, our interest will be focused on the trade-off between type I probability error control and power dissipation, estimated by the area under the ROC curve. We describe a technique for controlling the false discovery rate at an arbitrary level of error in testing multiple wavelet coefficients generated by a 2D discrete wavelet transform (DWT) of spatial maps of fMRI time series statistics. We also describe and apply change-point detection with recursive hypothesis testing methods that can be used to define a threshold unique to each level and orientation of the 2D-DWT, and Bayesian methods, incorporating a formal model for the anticipated sparseness of wavelet coefficients representing the signal or true image. The sensitivity and type I error control of these algorithms are comparatively evaluated by analysis of "null" images (acquired with the subject at rest) and an experimental data set acquired from five normal volunteers during an event-related finger movement task. We show that all three wavelet-based algorithms have good type I error control (the FDR method being most conservative) and generate plausible brain activation maps (the Bayesian method being most powerful). We also generalise the formal connection between wavelet-based methods for simultaneous multiresolution denoising/hypothesis testing and methods based on monoresolution Gaussian smoothing followed by statistical testing of brain activation maps.

Algorithms↗

PubMed1997

Beat by beat QT interval detection and characterization.

A novel algorithm for detection and analysis of QT interval, a risk factor for sudden cardiac death, is developed using the multiresolution wavelet approach. The characteristic points for detection of QT interval, i.e., the onset and offsets of the QRS complex and the T wave are detected by analyzing the wavelet transform of the ECG at particular scales. The results of the detailed study using standard data base indicate that proposed technique can be used to monitor critical heart patients for localization of problems in the duration of ventricular activation. The algorithm has been implemented on TMS320C25 based add-on DSP card to PC to provide the beat by beat analysis and display of QT interval data.

Algorithms↗

PubMed2001

Frequency decomposition and compounding of ultrasound medical images with wavelet packets.

Ultrasound beams propagating in biological tissues undergo distortions due to local inhomogeneities of the acoustic parameters and the nonlinearity of the medium. The spectral analysis of the radio-frequency (RF) backscattered signals may yield important clinical information in the field of tissue characterization, as well as enhancing the detectability of tissue parenchymal diseases. In this paper, we propose a new tissue spectral imaging technique based on the wavelet packets (WP) decomposition. In a conventional ultrasound imaging system, the received echo-signals are generally decimated to generate a medical image, with a loss of information. With the proposed approach, all the RF data are processed to generate a set of frequency subband images. The ultrasound echo signals are simultaneously frequency decomposed and decimated, by using two quadrature mirror filters, followed by a dyadic subsampling. In addition, to enhance the lesion detectability and the image quality, we apply a nonlinear filter to reduce noise in each subband image. The proposed method requires simple additional signal processing and it can be implemented on any real-time imaging system. The frequency subband images, which are available simultaneously, can be either used in a multispectral display or summed up together to reduce speckle noise. To localize the different frequency response in the tissues, we propose a multifrequency display method where three different subband images, chosen among those available, are encoded as red, green, and blue intensities (RGB) to create a false-colored RGB image. According to the clinical application, different choices can evidence different spectral proprieties in the biological tissue under investigation. To enhance the lesion contrast in a grey-level image, one of the possible methods is the summation of the images obtained from narrow frequency subbands, according to the frequency compounding technique. We show that by adding the denoised subband images created with the WP decomposition, the contrast-to-noise ratio in two phantom images is largely increased.

Image Processing, Computer-Assisted↗

PubMed2006

Combining algorithms in automatic detection of QRS complexes in ECG signals.

QRS complex and specifically R-Peak detection is the crucial first step in every automatic electrocardiogram analysis. Much work has been carried out in this field, using various methods ranging from filtering and threshold methods, through wavelet methods, to neural networks and others. Performance is generally good, but each method has situations where it fails. In this paper, we suggest an approach to automatically combine different QRS complex detection algorithms, here the Pan-Tompkins and wavelet algorithms, to benefit from the strengths of both methods. In particular, we introduce parameters allowing to balance the contribution of the individual algorithms; these parameters are estimated in a data-driven way. Experimental results and analysis are provided on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database. We show that our combination approach outperforms both individual algorithms.

Algorithms↗

PubMed1992

An adaptive method for tracking voicing irregularities.

A method has been developed for tracking irregularities in the acoustic waveform of a sustained phonation using the adaptive Wiener filter. Irregularities are determined by the technique of correlation cancellation. The algorithm is evaluated using sustained vowels produced by a formant synthesizer and by subjects with and without phonatary disorders. Results indicate that the method is capable of differentiating between normal and abnormal voices. Most significantly, however, it can also track sporadic or nonstationary irregularities in the shape of an individual acoustic wavelet. This method is expected to be a useful tool for the acoustics analysis of voice production.

Adult↗