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PubMed2006

Determination of dominant simulated spindle frequency with different methods.

Accurate analysis of EEG sleep spindle frequency is challenging. The frequency content of true sleep spindles is not known. Therefore, simulated spindle activity was studied in the present work. Five types of simulated test signals were designed, all containing a dominant spindle represented by a 13-Hz sine wave as such or with a waxing and waning pattern accompanied by a secondary spindle activity in three test signals. Background EEG was included in four test signals, modeled either as small additional sinusoids across the spindle frequency range or as filtered Gaussian noise segments. The purpose of this study was to investigate how accurately the dominant spindle frequency of 13 Hz could be resolved with different methods in the presence of the interfering waveforms. A matching pursuit (MP) based approach, discrete Fourier transform (DFT) with Hanning windowing with and without zero padding, Hankel total least squares (HTLS) and wavelet methods were compared in the analyses. MP method provided best overall performance, followed closely by DFT with zero padding. Comparative studies like this are important to decide the method of choice in clinical sleep EEG analysis.

Algorithms↗
PubMed2005

Ultrasonic data compression via parameter estimation.

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.

Algorithms↗
PubMed2006

Quantification and visualization of event-related changes in oscillatory brain activity in the time-frequency domain.

In this chapter we review the traditional approach for ERD/ERS quantification and a more recent approach based on wavelet transform. In particular, we address the visualization of these phenomena and the validation of the results through statistical significance testing. Furthermore, we report on preprocessing using independent component analysis (ICA) and introduce a novel ERD/ERS maximization method.

Algorithms↗
PubMed2000

Multiresolution spot detection by means of entropy thresholding.

Many imaging applications deal with the detection of small targets or spots embedded within an inhomogeneous background. We present a method that accomplishes a multiresolution detection on the wavelet-transformed image. The targets are separated from the background by the exploitation of Renyi's information, which is evaluated at the different decomposition levels of the wavelet transform. The scale-dependent candidate detections are successively combined by means of majority voting for final detection. Connections with results provided in different fields such as multifractal analysis, generalized information measures in scale-space, and cross-entropy analysis in fine-to-coarse transformations are discussed. Detection performance is investigated through an example from medical image analysis.

Diagnostic Imaging↗
PubMed1999

A comparison of wavelet and Joint Photographic Experts Group lossy compression methods applied to medical images.

This presentation focuses on the quantitative comparison of three lossy compression methods applied to a variety of 12-bit medical images. One Joint Photographic Exports Group (JPEG) and two wavelet algorithms were used on a population of 60 images. The medical images were obtained in Digital Imaging and Communications in Medicine (DICOM) file format and ranged in matrix size from 256 x 256 (magnetic resonance [MR]) to 2,560 x 2,048 (computed radiography [CR], digital radiography [DR], etc). The algorithms were applied to each image at multiple levels of compression such that comparable compressed file sizes were obtained at each level. Each compressed image was then decompressed and quantitative analysis was performed to compare each compressed-then-decompressed image with its corresponding original image. The statistical measures computed were sum of absolute differences, sum of squared differences, and peak signal-to-noise ratio (PSNR). Our results verify other research studies which show that wavelet compression yields better compression quality at constant compressed file sizes compared with JPEG. The DICOM standard does not yet include wavelet as a recognized lossy compression standard. For implementers and users to adopt wavelet technology as part of their image management and communication installations, there has to be significant differences in quality and compressibility compared with JPEG to justify expensive software licenses and the introduction of proprietary elements in the standard. Our study shows that different wavelet implementations vary in their capacity to differentiate themselves from the old, established lossy JPEG.

Algorithms↗
PubMed2005

Resampling fMRI time series.

The problem of selecting a threshold for the statistical parameter maps in functional MRI (fMRI) is a delicate issue. The use of advanced test statistics and/or the complex dependence structure of fMRI noise may preclude parametric statistical methods for finding appropriate thresholds. Non-parametric statistical methodology has been presented as a feasible alternative. In this paper, we discuss resampling methods for finding thresholds in single subject fMRI analysis. It is shown that the presence of a BOLD response in the time series biases the estimation of the temporal autocorrelation, which in turn leads to biased thresholds. Therefore, proposed resampling methods based on Fourier and wavelet transforms, which employ implicit and weak models of the temporal noise characteristic, may produce erroneous thresholds. In contrast, resampling based on a pre-whitening transform, which is driven by an explicit noise model, is robust to the presence of a BOLD response. The size of the bias is, however, largely dependent on the complexity of the experimental design. While blocked designs can induce large biases, event-related designs generate significantly smaller biases. Results supporting these claims are provided.

Artifacts↗
PubMed2005

Enhancing the signal-to-noise ratio of X-ray diffraction profiles by smoothed principal component analysis.

X-ray diffraction is one of the most widely applied methodologies for the in situ analysis of kinetic processes involving crystalline solids. However, due to its relatively high detection limit, it has only limited application in the context of crystallizations from liquids. Methods that can improve the detection limit of X-ray diffraction are therefore highly desirable. Signal processing approaches such as Savitzky-Golay, maximum likelihood, stochastic resonance, and wavelet transforms have been used previously to preprocess X-ray diffraction data. Since all these methods only utilize the frequency information contained in the single X-ray diffraction profile being processed to discriminate between the signals and the noise, they may not successfully identify very weak but important peaks especially when these weak signals are masked by severe noise. Smoothed principal component analysis (SPCA), which takes advantage of both the frequency information and the common variation within a set of profiles, is proposed as a methodology for the preprocessing of the X-ray diffraction data. Two X-ray diffraction data sets are used to demonstrate the effectiveness of the proposed approach. The first was obtained from mannitol-methanol suspensions, and the second data set was generated from slurries of L-glutamic acid (GA) in methanol. The results showed that SPCA can significantly improve the signal-to-noise ratio and hence lower the detection limits (approximately 0.389% g/mL for mannitol-methanol suspensions and 0.4 wt % for beta-form GA in GA-methanol slurries comprising mixtures of both alpha- and beta-forms of GA) thereby providing an important contribution to crystallization process performance monitoring.

Crystallization↗
PubMed1997

False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis.

We investigated the application of multiresolution global and local texture features to reduce false-positive detection in a computerized mass detection program. One hundred and sixty-eight digitized mammograms were randomly and equally divided into training and test groups. From these mammograms, two datasets were formed. The first dataset (manual) contained four regions of interest (ROIs) selected manually from each of the mammograms. One of the four ROIs contained a biopsy-proven mass and the other three contained normal parenchyma, including dense, mixed dense/fatty, and fatty tissues. The second dataset (hybrid) contained the manually extracted mass ROIs, along with normal tissue ROIs extracted by an automated Density-Weighted Contrast Enhancement (DWCE) algorithm as false-positive detections. A wavelet transform was used to decompose an ROI into several scales. Global texture features were derived from the low-pass coefficients in the wavelet transformed images. Local texture features were calculated from the suspicious object and the peripheral subregions. Linear discriminant models using effective features selected from the global, local, or combined feature spaces were established to maximize the separation between masses and normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classifier performance. The classification accuracy using global features were comparable to that using local features. With both global and local features, the average area, Az, under the test ROC curve, reached 0.92 for the manual dataset and 0.96 for the hybrid dataset, demonstrating statistically significant improvement over those obtained with global or local features alone. The results indicated the effectiveness of the combined global and local features in the classification of masses and normal tissue for false-positive reduction.

Biophysical Phenomena↗
PubMed1997

Wavelet transform in electrocardiography--data compression.

An application of the wavelet transform to electrocardiography is described in the paper. The transform is used as a first stage of a lossy compression algorithm for efficient coding of rest ECG signals. The proposed technique is based on the decomposition of the ECG signal into a set of basic functions covering the time-frequency domain. Thus, non-stationary character of ECG data is considered. Some of the time-frequency signal components are removed because of their low influence to signal characteristics. Resulting components are efficiently coded by quantization, composition into a sequence of coefficients and compression by a run-length coder and a entropic Huffman coder. The proposed wavelet-based compression algorithm can compress data to average code length about 1 bit/sample. The algorithm can be also implemented to a real-time processing system when wavelet transform is computed by fast linear filters described in the paper.

Acoustics↗
PubMed1998

Electrocortical activity in fetal sheep in the last seven days of gestation.

1. Electrocorticogram (ECoG) and myometrial electromyogram (EMG) were recorded continuously in chronically instrumented late gestation sheep fetuses (n = 9) to analyse: (1) 24 h ECoG patterns, (2) relationships between ECoG and myometrial contractility, and (3) 24 h ECoG patterns at the spontaneous onset of labour. 2. Spontaneous onset of labour was determined from the myometrial EMG. ECoG signals were purified by a denoising procedure of wavelet decomposition. High-voltage slow ECoG activity (HV) and low-voltage fast ECoG activity (LV) were determined mathematically, and HV-LV cycle periodicity was calculated by periodogram analysis. 3. Twenty-four hour rhythms were present in fetal ECoG HV-LV cycles in the 3-5 days prior to spontaneous onset of labour (P < 0.01). Cycle frequency was lower at 08.00-12.00 h and higher at 16.00-20.00 h (lights on at 07.00 h, lights off at 21.00 h). LV duration was longer at 20.00-24.00 h, and HV was shorter at 16.00-20.00 h. No relationship was found between ECoG cycles and myometrial contracture cycles. Twenty-four hour ECoG rhythms disappeared 1 day before the spontaneous onset of labour. 4. ECoG patterns changed 7 to 4 h prior to spontaneous onset of labour, percentage of time spent and duration of HV ECoG increased, and percentage of time spent in LV decreased significantly. ECoG HV-LV cyclicity was reduced 4-5 h prior to spontaneous onset of labour, indicating that the altered fetal hormonal and blood gas environment around the spontaneous onset of labour alter fetal neural function.

Animals↗
PubMed2002

The perception of breast cancer: what differentiates missed from reported cancers in mammography?

RATIONALE AND OBJECTIVES: Mammographers map endogenous and exogenous factors into decisions whether to report the presence of a malignant finding in a mammogram case. Thus, to understand how image-based elements are translated into observer-based decisions, the authors used spatial frequency analysis to model the areas on mammograms that attracted visual attention, in addition to the areas localized as abnormal. MATERIALS AND METHODS: Four mammographers read 40 two-view mammogram cases, of which 30 contained at least one malignant lesion visible on one or two views. Their eye positions were recorded during visual search. Once the mammographer felt confident enough to provide an initial impression of the case ("normal" or "abnormal"), the eye position monitoring was turned off and the mammographer indicated, with a mouse-controlled cursor, the location and nature of any malignant findings. Regions that elicited an overt or a covert response by the mammographers were extracted for processing by means of wavelet packets and artificial neural networks. RESULTS: Different decision outcomes yielded different energy representations, in the spatial frequency domain. These energy representations were used by an artificial neural network to predict decision outcome in areas of interest, derived from eye position analysis, on mammograms from new cases. Individual trends were observed for each mammographer. CONCLUSION: Spatial frequency representation of regions that attracted a given mammographer's visual attention may be useful for characterizing how that mammographer will respond to the visually selected areas.

Analysis of Variance↗
PubMed2025

Panaln: indexing pangenome for read alignment.

MOTIVATION: Pangenome indexing is a critical supporting technology in biological sequence analysis such as read alignment applications. The need to accurately identify billions of small sequencing fragments carrying sequencing errors and genomic variants drives the development of scalable and efficient pangenome indexing approach. RESULTS: We propose a new wavelet tree-based approach, called Panaln, for indexing pangenome and introduce a batch computation approach for fast count query over Panaln. We present a simple and effective seeding strategy and develop a pangenome program that uses the seed-and-extend paradigm for read alignment. Experimental results on simulated and real data demonstrate that Panaln uses significantly less space for the compared pangenome methods with generally higher accuracy. We provide a scalable index construction by representing pangenome with a linear model. Additionally, Panaln brings enhanced accuracy compared to the popular single reference methods. AVAILABILITY AND IMPLEMENTATION: Package: https://anaconda.org/bioconda/panaln and source code: https://github.com/Lilu-guo/Panaln.

Software↗
PubMed1994

Time-frequency analysis of ventricular late potentials.

The time-frequency characteristics are studied of averaged and filtered ECG records from 21 patients with sustained ventricular tachycardia and 29 healthy control subjects. Simulated data as well as real ECG records reveal the detection accuracy of the wavelet transform of signals with late potentials. The wavelet-transforms of preprocessed ECG signals are plotted in the time-frequency plane. These representations of the signals are well suited to describe the different characteristics of the patients and healthy subjects. A quantitative discrimination was performed with a sensitivity of 90% and a specificity of 72% by the energy underneath the squared modulus of the time-frequency distribution plots of the computed wavelet transforms.

Cardiac Pacing, Artificial↗
PubMed2006

Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

MOTIVATION: A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. RESULTS: Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. AVAILABILITY: The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.

Algorithms↗
PubMed2005

A system to detect the onset of epileptic seizures in scalp EEG.

OBJECTIVE: A new method for automatic seizure detection and onset warning is proposed. The system is based on determining the seizure probability of a section of EEG. Operation features a user-tuneable threshold to exploit the trade-off between sensitivity and detection delay and an acceptable false detection rate. METHODS: The system was designed using 652 h of scalp EEG, including 126 seizures in 28 patients. Wavelet decomposition, feature extraction and data segmentation were employed to compute the a priori probabilities required for the Bayesian formulation used in training, testing and operation. RESULTS: Results based on the analysis of separate testing data (360 h of scalp EEG, including 69 seizures in 16 patients) initially show a sensitivity of 77.9%, a false detection rate of 0.86/h and a median detection delay of 9.8 s. Results after use of the tuning mechanism show a sensitivity of 76.0%, a false detection rate of 0.34/h and a median detection delay of 10 s. Missed seizures are characterized mainly by subtle or focal activity, mixed frequencies, short duration or some combination of these traits. False detections are mainly caused by short bursts of rhythmic activity, rapid eye blinking and EMG artifact caused by chewing. Evaluation of the traditional seizure detection method of using both data sets shows a sensitivity of 50.1%, a false detection rate of 0.5/h and a median detection delay of 14.3 s. CONCLUSIONS: The system performed well enough to be considered for use within a clinical setting. In patients having an unacceptable level of false detection, the tuning mechanism provided an important reduction in false detections with minimal loss of detection sensitivity and detection delay. SIGNIFICANCE: During prolonged EEG monitoring of epileptic patients, the continuous recording may be marked where seizures are likely to have taken place. Several methods of automatic seizure detection exist, but few can operate as an on-line seizure alert system. We propose a seizure detection system that can alert medical staff to the onset of a seizure and hence improve clinical diagnosis.

Artifacts↗
PubMed2000

Computer aided detection of microcalcifications in digital mammograms.

Microcalcification detection is widely used for early diagnosis of breast cancer. Nevertheless, mammogram visual analysis is a complex task for expert radiologists. In this paper, we present a new method for computer aided detection of microcalcifications in digital mammograms. The detection is performed on the wavelet transformed image. The calcifications are separated from the background by exploiting the evaluation of Renyi's information at the different decomposition levels of the wavelet transform. Experiments are performed on a standard and publicly available dataset and the results are evaluated with respect to recent achievements reported in the literature.

Algorithms↗
PubMed2006

[Fast algorithm for feature extraction and identification of infrared spectra of polluted gases].

With the multi-resolution analysis, features of infrared spectra of polluted gases were extracted. Then the data were trained or identified by a neural network system. The experimental results show that the combination of the wavelet transform and the neural network has the great ability of feature extracting. And the system is quite efficient for identifying infrared spectra.

English Abstract↗