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PubMed2000

Wavelet analysis of transient biomedical signals and its application to detection of epileptiform activity in the EEG.

Wavelet based signal analysis provides a powerful new means for the analysis of nonstationary signals such as the human EEG. The properties of the discrete wavelet transform are reviewed in illustrated application examples. The continuous wavelet transform is shown to provide better detection and representation of isolated transients. An approach to extract features of edges and transients from the continuous wavelet transform is outlined. Matching pursuit is presented as a more general transform method that covers both transients and oscillation spindles. A statistical model for the continuous wavelet transform of background EEG is found. A spike detection system based on this background model is presented. The performance of this detection system has been assessed in a preliminary clinical study of 11 EEG recordings containing epileptiform activity and shown to have a sensitivity of 84% and a selectivity of 12%. The spatial context of epileptiform activity will be incorporated to improve system performance.

Brain↗
PubMed2003

Uterine contractility signals--an introduction to wavelet analysis.

PURPOSE: Analysis of the uterine contractility in the nonpregnant states has provided information about physiological changes during menstrual cycle. There is need to develop methods of recording uterine activity as well as mathematical interpretation of recorded time series. Wavelets are a new powerful tool for signal and image processing. The aim of this study is an introductory view of Fourier (one of the fundamental methods of investigating of biomedical signals) and wavelet transforms applications in the analysis of uterine contractions. MATERIAL AND METHODS: Spontaneous uterine activity of healthy patient and patient with dysmenorrhea was recorded by micro-tip two sensors catheter (Millar Instruments, Inc. USA). After amplification analogue signals were converted to digital. Signals were analysed using Fourier and wavelet transforms. RESULTS: Contrary to the Fourier decomposition, which is global and provides the information integrated over the whole signal, the continuous and discrete wavelet transforms allow to extract local and global variations of the recorded contractions. From the analysis of the coefficients of the wavelet transform we can assess various pattern of propagation: normal propagation, simultaneous propagation and inverted propagation. CONCLUSIONS: This study is the introduction to the wavelet analysis of the uterine contraction signals. Wavelet transform provides insight into the structure of the time series at various scales. It allows to localise changes of the signal in time, providing additional information in comparison with the Fourier transform.

Dysmenorrhea↗
PubMed2005

Assessment of wavelet analysis of gait in children with typical development and cerebral palsy.

The objective of this study was to examine the use of the continuous wavelet transform (CWT) on surface electromyographic (sEMG) signals acquired from the lower extremity muscles during gait in children with typical development (TD) and cerebral palsy (CP). This was done to explore the possibility of developing a quantitative assessment scale of motor function based on time-frequency information. An initial study was conducted on retrospective gait data from three children, matched in gender and in anthropometric variables but with differing levels of walking ability. EMG data were extracted from five lower extremity muscles to assess the degrees of differentiation. The data were processed using the CWT to derive an average scalogram, from which the instantaneous mean frequency (IMNF) was calculated. Principal component analysis was used to assess the differences between the curves. Preliminary results indicated that for select lower extremity muscles, there was a significant deviation in the IMNF curves in the child with CP as compared to the child with TD. Furthermore, as motor impairment increased, total percent explained variance to the TD curves decreased. This suggests that it might be possible to derive a physiologically based quantitative index for assessing motor function and for assessing clinical treatments in CP using the wavelet analysis.

Algorithms↗
PubMed2001

[The application of wavelet analysis of remote detection of pollution clouds].

The discrete wavelet transform (DWT) is used to analyse the spectra of pollution clouds in complicated environment and extract the small-features. The DWT is a time-frequency analysis technology, which detects the subtle small changes in the target spectrum. The results show that the DWT is a quite effective method to extract features of target-cloud and improve the reliability of monitoring alarm system.

Air Pollutants↗
PubMed2005

Identification of plasma membrane macro- and microdomains from wavelet analysis of FRET microscopy.

In this study, we sought to characterize functional signaling domains by applying the multiresolution properties of the continuous wavelet transform to fluorescence resonance energy transfer (FRET) microscopic images of plasma membranes. A genetically encoded FRET reporter of protein kinase C (PKC)-dependent phosphorylation was expressed in COS1 cells. Differences between wavelet coefficient matrices revealed several heterogeneous domains (typically ranging from 1 to 5 microm), reflecting the dynamic balance between PKC and phosphatase activity during stimulation with phorbol-12,13-dibutyrate or acetylcholine. The balance in these domains was not necessarily reflected in the overall plasma membrane changes, and observed heterogeneity was absent when cells were exposed to a phosphatase or PKC inhibitor. Prolonged exposure to phorbol-12,13-dibutyrate and acetylcholine yielded more homogeneous FRET distribution in plasma membranes. The proposed wavelet-based image analysis provides, for the first time, a basis and a means of detecting and quantifying dynamic changes in functional signaling domains, and may find broader application in studying fine aspects of cellular signaling by various imaging reporters.

Adaptor Proteins, Signal Transducing↗
PubMed2005

Wavelet analysis and scaling properties of time series.

We propose a wavelet based method for the characterization of the scaling behavior of nonstationary time series. It makes use of the built-in ability of the wavelets for capturing the trends in a data set, in variable window sizes. Discrete wavelets from the Daubechies family are used to illustrate the efficacy of this procedure. After studying binomial multifractal time series with the present and earlier approaches of detrending for comparison, we analyze the time series of averaged spin density in the 2D Ising model at the critical temperature, along with several experimental data sets possessing multifractal behavior.

Journal Article↗
PubMed2007

Incipient cardiovascular autonomic imbalance revealed by wavelet analysis of heart rate variability in Type 2 diabetic patients.

AIM: Incipient cardiovascular autonomic imbalance is not readily diagnosed by conventional methods. Spectral analysis of heart rate variability (HRV) by wavelet transform (WT) was used to measure cardiovascular autonomic function in patients with Type 2 diabetes. METHODS: Thirty-two diabetic patients without (D), 26 with cardiovascular autonomic neuropathy (DAN) and 72 control subjects (C) participated. A 30-min HRV time series was analysed by wavelet transformation and four characteristic frequency intervals were defined: I (0.0095-0.021 Hz), II (0.021-0.052 Hz), III (0.052-0.145 Hz) and IV (0.145-0.6 Hz). RESULTS: When compared with C, in both D and DAN the normalized power and amplitude of interval II were increased and of interval IV decreased, resulting in a significantly higher II/IV ratio. Furthermore, in DAN the normalized power and amplitude of interval I were increased and of interval III decreased when compared with the D and C groups. The diabetic patients were divided in two equal subgroups according to HbA(1c) < 8.0% and >or= 8.0%. In the subgroup with HbA(1c) >or= 8.0%, normalized power in interval II was significantly higher and in interval IV significantly lower than in the subgroup with HbA(1c) < 8.0%. In D, but not in DAN patients prescribed ACE inhibitors, the absolute amplitude and power of oscillations were significantly higher than in patients not taking ACE inhibitor therapy. CONCLUSIONS: Patients with diabetes have increased sympathetic and decreased parasympathetic cardiac activity regardless of the presence of autonomic neuropathy. Glycaemic control and treatment with ACE inhibitors may favourably influence HRV in diabetic patients without autonomic neuropathy.

Aged↗
PubMed2005

Detection of heart murmurs using wavelet analysis and artificial neural networks.

This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.

Algorithms↗
PubMed2006

Significant EEG features involved in mathematical reasoning: evidence from wavelet analysis.

Using electroencephalographic (EEG) signals and a novel methodology based on wavelet measures in the time-scale domain, we evaluated cortex reactions during mathematical thinking. Our purpose was to extract more precise information from the cortex reactions during this cognitive task. Initially, the brain areas (lobes) of significant activation during the task are extracted using time-averaged wavelet power spectrum estimation. Then, a refinement step makes use of statistical significance-based criteria for comparing wavelet power spectra between the task and the rest condition. EEG signals are recorded from 15 young normal volunteers using 30 scalp electrodes as participants performed one difficult arithmetic task and the results are compared with a rest situation. The results are in accordance with similar previous studies, showing activations of frontal and central regions. Compared with the alternative spectral-based techniques, the method we propose achieves higher task discrimination on the same dataset and provides additional detail-signal information to evaluate cortical reactivity during local cortical activation.

Adult↗
PubMedDate not supplied

Continuous wavelet analysis as an aid in the representation and interpretation of electrogastrographic signals.

This paper presents an application of the continuous wavelet transform (CWT) in the analysis of electrogastrographic (EGG) signals. Due to the nonstationary nature of EGG signals, the CWT method, which uses multiresolution scaled windows, gives a better time-frequency resolution than the short-time Fourier transform, which uses a fixed window. Spike activity due to gastric contraction was investigated through experiments on dogs. During spike activity we observed an increase in magnitude of the slow wave and the appearance of a low frequency component with half the frequency of the slow wave. Studies of the EGG signals from the small intestine are also presented to investigate the hypothesis that its slow wave might be confounded with spike activity in the stomach due to the similarity of their frequency ranges.

Animals↗
PubMed2006

Wavelet analysis for detecting body-movement artifacts in optical topography signals.

We have developed a wavelet-based method of detecting body-movement artifacts in optical topography (OT) signals. Although OT, which is a noninvasive imaging technique for measuring hemodynamic response related to brain activation, is particularly useful for studying infants, the signals occasionally contain undesirable artifacts caused by body movements, so data corrupted by body-movement artifacts must be eliminated to obtain reliable results. For this purpose, we applied a wavelet transform to automatically detect body-movement artifacts in OT signals. We measured OT signals from nine healthy infants in response to speech stimuli. After the continuous signals had been divided into blocks (a block is a time series of OT signal in a 30-s period including a 10-s stimulation period), they were classified into two groups (movement blocks and non-movement blocks) according to whether the participants moved or not by video judgment. Using those data, we developed a wavelet-based algorithm for detecting body-movement artifacts at a high discrimination rate being consistent with the actual body-movement state. The wavelet method has two parameters (scale and threshold), and a Monte Carlo analysis gave the mean optimal parameters as 9+/-1.9 (mean+/-standard deviation) for the scale and as 42.7+/-1.9 for the threshold. Our wavelet method with the mean optimal parameters (scale=9, threshold=43) achieved a higher discrimination rate (mean+/-standard deviation: 86.3+/-8.8%) for actual body movement than a previous method (mean+/-standard deviation: 80.6+/-8.7%) among different participants (paired t test: t(8)=2.92, p<0.05). These results demonstrate that our wavelet method is useful in practice for eliminating blocks containing body-movement artifacts in OT signals. It will contribute to obtaining reliable results from OT studies of infants.

Artifacts↗
PubMed2002

Interscale wavelet maximum-a fine to coarse algorithm for wavelet analysis of the EMG interference pattern.

A method has been developed, interscale wavelet maximum (ISWM), for characterising the electromyogram (EMG) interference pattern to assist in the diagnosis of neuromuscular disease. EMG signals are decomposed with the redundant dyadic wavelet transform and wavelet maxima (WM) are found. Thresholding methods are applied to remove WM due to noise and background activity. An efficient fine-to-coarse algorithm identifies the WM tree structure for the motor unit action potential rising edges. The WM for each tree are summed at each scale; the largest value is the ISWM. Highly significant differences in ISWM values have been found between healthy, myopathic, and neuropathic subjects that could make the technique a useful diagnostic tool.

Action Potentials↗
PubMed1999

Wavelet analysis of oscillations in the peripheral blood circulation measured by laser Doppler technique.

The wavelet transform technique, a time-frequency method with logarithmic frequency resolution, was used to analyze oscillations in human peripheral blood flow measured by laser Doppler flowmetry. The oscillations extended over a wide frequency scale and their periods varied in time. Within the frequency range studied, 0.0095-1.6 Hz, five characteristic oscillations were revealed, arising from both local and central regulatory mechanisms. After the insertion of endothelium-dependent and endothelium-independent vasodilators the spectra of blood flow markedly differed in the frequency interval 0.0095-0.02 Hz. In this way it was demonstrated that endothelial activity is a rhythmic process that contributes to oscillations in blood flow with a characteristic frequency of around 0.01 Hz. The study illustrates the potential of laser Doppler flowmetry combined with dynamical systems analysis for studies of both the micro- and macroscopic mechanisms of blood flow regulation in vivo.

Acetylcholine↗
PubMed2003

Wavelet analysis can sensitively describe dynamics of ethanol evoked local field potentials of the slug (Limax marginatus) brain.

Odorants evoke characteristic, but complex, local field potentials (LFPs) in the molluscan brain. Wavelet tools in combination with Fourier analysis can detect and characterize hitherto unknown discrete, slow potentials underlying the conspicuous oscillations. Ethanol was one of the odorants that we have extensively studied (J. Neurosci. Methods, 119 (2002) 89). To detect new features and to elucidate their functions, we tested the wavelet tools on the ethanol-evoked LFP responses of the slug (Limax) procerebrum. Recordings were made in vitro from the neuropile and the cell layer. The present study led to the following findings: (i) Mutual exclusion. Energy concentrated mainly in two ranges, (a) 0.1-0.4 Hz and (b) 1.56-12.5 Hz, and the sum of energy remained constant throughout experiments regardless of the condition. A redistribution of relative energy within this sum seemed to occur in the course of main, possible interactions between the two components excluding each other ('mutual exclusion'). (ii) Transient signal ordering and disordering. Ethanol stimulation alternatingly evoked periods of strongly time evolving oscillation dominated by the energy of 1.56-12.5 Hz (increase of entropy=disordered or complexly ordered state) and those of near-silence were predominated by the energy of 0.1-0.4 Hz (decrease of entropy=ordered state). (iii) About 0.1 Hz slow wave oscillation. It was robust. The dominant energy oscillation and the resulting large entropy fluctuation were negatively correlated to each other, and revealed strong frequency-tuning or synchronization at this frequency. Our findings suggest that discrete slow waves play functionally important roles in the invertebrate brain, as widely known in vertebrate EEG. Wavelet tools allow an easy interpretation of several minutes of frequency variations in a single display and give precise information on stimulus-evoked complex change of the neural system describing the new state 'more ordered' or 'non-ordered or more complexly ordered'.

Animals↗
PubMed2003

Predicting sleep apnoea syndrome from heart period: a time-frequency wavelet analysis.

Heart rate fluctuations are a typical finding during obstructive sleep apnoea, characterised by bradycardia during the apnoeic phase and tachycardia at the restoration of ventilation. In this study, a time-frequency domain analysis of the nocturnal heart rate variability (HRV) was evaluated as the single diagnostic marker for obstructive sleep apnoea syndrome (OSAS). The predictive accuracy of time-frequency HRV variables (wavelet (Wv) decomposition parameters from level 2 (Wv2) to level 256 (Wv256)) obtained from nocturnal electrocardiogram Holter monitoring were analysed in 147 consecutive patients aged 53.8+/-11.2 yrs referred for possible OSAS. OSAS was diagnosed in 66 patients (44.9%) according to an apnoea/hypopnoea index > or = 10. Using receiver-operating characteristic curves analysis, the most powerful predictor variable was Wv32 (W 0.758, p<0.0001), followed by Wv16 (W 0.729, p<0.0001) and Wv64 (W 0.700, p<0.0001). Classification and Regression Trees methodology generated a decision tree for OSAS prediction including all levels of Wv coefficients, from Wv2 to Wv256 with a sensitivity reaching 92.4% and a specificity of 90.1% (percentage of agreement 91.2%) with this nonparametric analysis. Time-frequency parameters calculated using wavelet transform and extracted from the nocturnal heart period analysis appeared as powerful tools for obstructive sleep apnoea syndrome diagnosis.

Adult↗
PubMed2006

Quantifying cortical activity during general anesthesia using wavelet analysis.

This paper reports on a novel method for quantifying the cortical activity of a patient during general anesthesia as a surrogate measure of the patient's level of consciousness. The proposed technique is based on the analysis of a single-channel (frontal) electroencephalogram (EEG) signal using stationary wavelet transform (SWT). The wavelet coefficients calculated from the EEG are pooled into a statistical representation, which is then compared to two well-defined states: the awake state with normal EEG activity, and the isoelectric state with maximal cortical depression. The resulting index, referred to as the wavelet-based anesthetic value for central nervous system monitoring (WAV(CNS)), quantifies the depth of consciousness between these two extremes. To validate the proposed technique, we present a clinical study which explores the advantages of the WAV(CNS) in comparison with the BIS monitor (Aspect Medical Systems, MA), currently a reference in consciousness monitoring. Results show that the WAV(CNS) and BIS are well correlated (r = 0.969) during periods of steady-state despite fundamental algorithmic differences. However, in terms of dynamic behavior, the WAV(CNS) offers faster tracking of transitory changes at induction and emergence, with an average lead of 15-30 s. Furthermore, and conversely to the BIS, the WAV(CNS) regains its preinduction baseline value when patients are responding to verbal command after emergence from anesthesia. We conclude that the proposed analysis technique is an attractive alternative to BIS monitoring. In addition, we show that the WAV(CNS) dynamics can be modeled as a linear time invariant transfer function. This index is, therefore, well suited for use as a feedback sensor in advisory systems, closed-loop control schemes, and for the identification of the pharmacodynamic models of anesthetic drugs.

Adult↗
PubMed2004

Spatiotemporal wavelet analysis for functional MRI.

Characterizing the spatiotemporal behavior of the BOLD signal in functional Magnetic Resonance Imaging (fMRI) is a central issue in understanding brain function. While the nature of functional activation clusters is fundamentally heterogeneous, many current analysis approaches use spatially invariant models that can degrade anatomic boundaries and distort the underlying spatiotemporal signal. Furthermore, few analysis approaches use true spatiotemporal continuity in their statistical formulations. To address these issues, we present a novel spatiotemporal wavelet procedure that uses a stimulus-convolved hemodynamic signal plus correlated noise model. The wavelet fits, computed by spatially constrained maximum-likelihood estimation, provide efficient multiscale representations of heterogeneous brain structures and give well-identified, parsimonious spatial activation estimates that are modulated by the temporal fMRI dynamics. In a study of both simulated data and actual fMRI memory task experiments, our new method gave lower mean-squared error and seemed to result in more localized fMRI activation maps compared to models using standard wavelet or smoothing techniques. Our spatiotemporal wavelet framework suggests a useful tool for the analysis of fMRI studies.

Algorithms↗
PubMed2000

Spectral components of heart rate variability determined by wavelet analysis.

Spectral components of heart rate variability (HRV) are determined in the time-frequency domain using a wavelet transform. Based on the finer estimation of low-frequency content enabled by the logarithmic resolution of the wavelet transform, corrections of spectral intervals, already defined by Fourier and model based methods, are proposed. The characteristic peaks between 0.0095 and 0.6 Hz are traced in time and four spectral intervals are defined, I (0.0095-0.021 Hz), II (0.021-0.052 Hz), III (0.052-0.145 Hz) and IV (0.145-0.6 Hz), within which peaks are located for all subjects included. These intervals are shown to be invariant regardless of the age and the state of the system. We also show that the frequency and power of the spectral components are related to age, AMI and particularly to type II diabetes mellitus.

Adult↗