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PubMed1984

Detection of periodic signals in noise: an iterative procedure.

A method for detection and estimation of periodic signals in the presence of noise is described. The algorithm is an iterative improvement of the autoregressive-moving average estimation of a stochastic process and gives an exact frequency resolution of sinusoidal signals additively mixed with noise in a low signal-to-noise ratio only from a small number of measurements. The iterative improvement of the spectral estimation compared with other methods is demonstrated by examples.

Computers↗
PubMed2001

The short-time Fourier transform and muscle fatigue assessment in dynamic contractions.

The mean frequency of the power spectrum of an electromyographic signal is an accepted index for monitoring fatigue in static contractions. There is however, indication that it may be a useful index even in dynamic contractions in which muscle length and/or force may vary. The objective of this investigation was to explore this possibility. An examination of the effects of amplitude modulation on modeled electromyographic signals revealed that changes in variance created in this way do not sufficiently affect characteristic frequency data to obscure a trend with fatigue. This validated the contention that not all non-stationarities in signals necessarily manifest in power spectral parameters. While an investigation of the nature and effects of non-stationarities in real electromyographic signals produced from dynamic contractions indicated that a more complex model is warranted, the results also indicated that averaging associated with estimating spectral parameters with the short-time Fourier transform can control the effects of the more complex non-stationarities. Finally, a fatigue test involving dynamic contractions at a force level under 30% of peak voluntary dynamic range, validated that it was possible to track fatigue in dynamic contractions using a traditional short-time Fourier transform methodology.

Adult↗
PubMed2003

Spectral analysis of the acoustic emission of laser-produced plasmas.

A Q-switched frequency Nd:YAG laser was focused on copper, aluminum, and lead targets. The acoustic emission accompanying plasma formation was acquired and analyzed in both the time and the frequency domains. Spectral analysis of the shock wave has proved to be a simple and low-cost diagnostic of plasma phenomena. In the time domain, several propagation mechanisms of the shock wave were observed and the velocity profile of the shock wave estimated. Spectral measurements were performed in the acoustic propagation regime of the shock waves. Spectral features related to the plasma formation mechanism were identified and discussed for copper, aluminum, and lead on the basis of the physical properties of these elements, the expansion mechanisms of the plasma, and an empirical parameter representative of the transported energy.

Journal Article↗
PubMed2000

Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment.

In this article we consider the application of parametric spectral analysis to multichannel event-related potentials (ERPs) during cognitive experiments. We show that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series. We propose a bootstrap procedure to assess the variability in the estimated spectral quantities. Finally, we apply AMVAR spectral analysis to a visuomotor integration task, revealing rapidly changing cortical dynamics during different stages of task processing.

Animals↗
PubMed2000

Factor analysis of confocal image sequences of human papillomavirus DNA revealed with fast red in cervical tissue sections stained with TOTO-iodide.

OBJECTIVE: To visualize and localize specific DNA sequences by fluorescence in situ hybridization, confocal laser scanning microscopy (CLSM) and factor analysis of biomedical image sequences (FAMIS). STUDY DESIGN: Human papillomavirus (HPV) DNA was identified in cervical tissue sections with biotinylated DNA probes recognizing the whole genome of HPV DNA types 18 and 16, and DNA-DNA hybrids were revealed by streptavidin-alkaline phosphatase and Fast Red (FR). Cell nuclei were counterstained with TOTO-iodide. Image sequences were obtained using successive dynamic or spectral sequences of images on different optical slices from CLSM. The location of fluorescent signals inside tissue preparations was determined by FAMIS and/or selection of filters at emission. Image sequences were summarized into a reduced number of images, called "factor images," and curves, called "factors." Factors estimate spectral patterns and depth emission profiles. Factor images correspond to spatial distributions of the different factors. RESULTS: We distinguished between FR and nucleus staining in HPV DNA hybridization signals by taking into account differences in their spectral patterns and improved visualization by taking into account differences in their focus (depth emission profiles). CONCLUSION: FAMIS, together with CLSM, made possible the detection and characterization of HPV DNA sequences in cells of cervical tissue sections.

Azo Compounds↗
PubMed1988

Estimation of running frequency spectra using a Kalman filter algorithm.

A method is suggested for the computation of running frequency spectra from non-stationary oscillations in a long time series. The method is based on an autoregressive model where the coefficients are assumed to vary slowly. The coefficients are updated using the Kalman filter technique. The method is shown to be superior to ordinary autoregressive spectral estimation based on stationary theory in recognizing rapid changes in the frequencies of oscillations.

Algorithms↗
PubMed1991

Biomedical signal processing (in four parts). Part 3. The power spectrum and coherence function.

This is the third in a series of four tutorial papers on biomedical signal processing and concerns the estimation of the power spectrum (PS) and coherence function (CF) od biomedical data. The PS is introduced and its estimation by means of the discrete Fourier transform is considered in terms of the problem of resolution in the frequency domain. The periodogram is introduced and its variance, bias and the effects of windowing and smoothing are considered. The use of the autocovariance function as a stage in power spectral estimation is described and the effects of windows in the autocorrelation domain are compared with the related effects of windows in the original time domain. The concept of coherence is introduced and the many ways in which coherence functions might be estimated are considered.

Biomedical Engineering↗
PubMedDate not supplied

Performance evaluation of implantable artificial organs by sound spectrum analysis.

In this paper, a sound spectrum analyzing method was proposed to pre detect malfunctions of implantable artificial organs, such as an electromechanical total artificial heart (TAH) or prosthetic valves, without any percutaneous invasion. For this purpose, a sound detecting device was developed using a high sensitivity condenser microphone with a frequency range of more than 13 kHz. Output signals of this device are sampled at 100 kHz maximally, and sampled data are stored in an IBM PC (SamBo, Korea). To remove environmental noises in the measured sound, an adaptive least-mean square algorithm was employed. Using the squared value of the sound signal, the best position where only sounds from mechanical components can be measured was found. The sound spectrum was obtained by the periodogram spectral estimating method. Experiments were performed with this system, and the results indicated that: 1) by using an adaptive noise cancelling algorithm, a more noise-free signal can be obtained; 2) the harmonics from the mechanical components of a pendulum type electromechanical TAH were approximately 1.3 KHz; 3) a spectral change was observed when we compared the power spectral densities of a normal and failed TAH; 4) the spectral shift to higher harmonics occurred with an increase in heart rate; and 5) the sound propagation properties of tissue were investigated with animal experiments. The method proposed was found to be applicable in the detection of implantable artificial organ mechanical failure by sound spectrum analysis without the need for percutaneous invasion.

Acoustics↗
PubMed1992

Advanced time-frequency methods for signal-averaged ECG analysis.

Frequency-domain techniques have been extensively investigated for the analysis of high-resolution electrocardiograms (ECGs), although the merit of frequency-domain analysis is still subject to controversy. Time-frequency analysis methods, which estimate the frequency content of a signal as a function of time, potentially provide even more information for improved ECG analysis. Some researchers report impressive results in predicting the outcome of electrophysiologic studies using the short-time Fourier transform (spectrogram). Other time-frequency representations, such as the Wigner distribution, short-time spectral estimators, and the wavelet transform, have also been investigated. The authors present a unified overview of time-frequency representations, showing that only four classes characterize most time-frequency representations. The authors describe the advantages and drawbacks of the various approaches and speculate on their promise for ECG analysis. Very preliminary experiments in applying some of these techniques to the prediction of the outcome of electrophysiologic studies have suggested some possible new research directions.

Electrocardiography↗
PubMed1994

Parameter evaluation of the inverse power-law spectrum of heart rate. A quantitative approach for ECG arrhythmia analysis.

A preliminary study was performed to ensure reliable R-wave detection and fiducial mark location. A generalized phase-sampling model for spectral estimation was then used, based on an interest in beat-to-beat variations. Heart rate spectrum characteristics of 32 presumed-normal subjects and 44 records from the MIT-BIH electrocardiographic database (Massachusetts Institute of Technology, Cambridge, MA) were analyzed and three parameters were calculated: regression line slope, intercept, and cross correlation between the spectral data and the regression lines. Evaluation of these inverse power-law spectrum parameters provides a potential quantitative approach for characterizing erratic fluctuations of heart rate and is possibly used to distinguish between healthy and abnormal subjects. From the 44 recordings in the MIT-BIH database, a V-shaped curve was found in a plot of the regression line slope versus cross correlation. The results for both unmedicated and medicated patients with normal sinus rhythm cluster in the top left region of the graph. Also, 29 of the presumed-normal subjects cluster in the same region. Patients with premature ventricular contraction beats cluster in the top right region of the V-shaped curve. The rest of the recordings from the variety of arrhythmia cases in the database have low slopes and cross correlations, so they cluster near the apex of the V-shaped curve. Three volunteers who each had more than 32 atrial premature contraction and premature ventricular contraction beats also fall in this region of the graph. The results from 10 young volunteers from the United States and 15 volunteers from seven other countries cluster into different regions.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed2002

A study on the optimum order of autoregressive models for heart rate variability.

Heart rate variability (HRV) has been used as a non-invasive marker of the activity of the autonomic nervous system and its spectrum analysis gives a measure of the sympatho-vagal balance. If short segments are used in an attempt to improve temporal resolution, autoregressive spectral estimation, where the mode] order must be estimated, is preferred. In this paper we compare four criteria for the estimation of the 'optimum' model order for an autoregressive (AR) process applied to short segments of tachograms used for HRV analysis. The criteria used were Akaike's final prediction error, Akaike's information criterion, Parzen's criterion of autoregressive transfer function and Rissanen's minimum description length method, and they were first applied to tachograms to verify (i) the range and distribution of model orders obtained and (ii) if the different techniques suggest the same model order for the same frames. The four techniques were then tested using a true AR process of known order p = 6; this verified the ability of the criteria to estimate the correct order of a true AR process and the effect, on the spectrum, of choosing a wrong model order was also investigated. It was found that all the four criteria underestimate the true AR order; specifying a fixed model order was then looked at and it is recommended that an AR order not less than p = 16, should be used for spectral analysis of short segments of tachograms.

Adult↗
PubMed2006

Image reconstruction: a unifying model for resolution enhancement and data extrapolation. Tutorial.

In reconstructing an object function F(r) from finitely many noisy linear-functional values integral of F(r)Gn(r)dr we face the problem that finite data, noisy or not, are insufficient to specify F(r) uniquely. Estimates based on the finite data may succeed in recovering broad features of F(r), but may fail to resolve important detail. Linear and nonlinear, model-based data extrapolation procedures can be used to improve resolution, but at the cost of sensitivity to noise. To estimate linear-functional values of F(r) that have not been measured from those that have been, we need to employ prior information about the object F(r), such as support information or, more generally, estimates of the overall profile of F(r). One way to do this is through minimum-weighted-norm (MWN) estimation, with the prior information used to determine the weights. The MWN approach extends the Gerchberg-Papoulis band-limited extrapolation method and is closely related to matched-filter linear detection, the approximation of the Wiener filter, and to iterative Shannon-entropy-maximization algorithms. Non-linear versions of the MWN method extend the noniterative, Burg, maximum-entropy spectral-estimation procedure.

Algorithms↗
PubMed2005

[Relations of the EEG local and spatialtemporal spectral characteristics changes under hypoxia in humans].

Spatial temporal and local EEG characteristics were studied in healthy subjects during inhalation of hypoxic oxygen-nitrogen gas mixture with 8 % content of oxygen. Analysis of spectra power density, coherence, phase shift, similarity of dominant frequencies in the EEGs of different derivations was performed separately for the EEG epochs with and without visually detected patterns of spatial synchrony of the EEG. Apart from this, a fact of dominance of the frequency in the EEG spectra of corresponding derivation was taken into account when estimating spectral parameters. Results of the study showed that, in general, under hypoxia, the EEG coherence in alpha- and delta-frequency range decreases as compared to the background level, in beta-range growth of this parameter is observed, in theta-range ambiguous changes occur: in the epochs with patterns of spatial synchrony--growth, in other epochs--lowering. Under hypoxia, also occurs growth of frontal and temporal EEGs' phase shift (corresponding to EEGs other derivations) in delta- and theta-range. In beta-range, on the contrary, average level of the phase shift decreases. It was revealed that taking into account the fact of dominance of frequency in the local EEG spectra is necessary for correct interpretation of the EEG spatial and temporal parameter analysis' results. A mathematical model of interaction between processes with different frequency characteristics is suggested, which explains some facts obtained in the study.

Adolescent↗
PubMed1994

Quantification of two-dimensional NOE spectra via a combined linear and nonlinear least-squares fit.

Determining the volumes of peaks in 2D NMR spectra can be prohibitively difficult in cases of overlapping, broad lines. Deconvolution and parameter estimation can be attempted on either the time-domain or the frequency-domain data. We present a method of estimating spectral parameters from frequency-domain data, using a combination of Lorentzian and Gaussian lineshapes for reference lines. This approach combines a previously published method of projecting the data on a linear space spanned by reference lines with a nonlinear least-squares fitting algorithm. Comparison of this method with other published methods of frequency-domain deconvolution shows that it is both more precise and more accurate when estimating 2D volumes.

Algorithms↗
PubMed2000

Assessment of occupational exposure patterns by frequency-domain analysis of time series data.

Laboratory evidence increasingly points to exposure pattern characteristics, including the duration, frequency, and timing of the exposure during the day, as important factors influencing the biological response to extremely low-frequency magnetic fields. An exploratory analysis of exposure patterns was conducted in 113 electric utility workers employed as electricians, cable splicers, line workers, and power plant operators. The purpose of the study was to describe extremely low-frequency magnetic field exposure pattern characteristics of electric utility workers and evaluate grouping strategies for classifying occupational exposures based on their exposure pattern characteristics. Exposure patterns describe the cyclic fluctuation in exposures over time, and were evaluated by partitioning the variation of the time series into frequency components using frequency-domain analysis of the transformed and processed time series. The study samples were classified using traditional grouping strategies based on occupation and time-weighted average (TWA), and non-traditional grouping strategies based on cluster analysis of the standardized, low-frequency exposure pattern components. Rules for classifying samples into each group were developed using linear discriminant analysis, with the performance of each grouping strategy evaluated using a crossvalidation study design to estimate the rate of misclassification. Exposure patterns appeared unrelated to grouping strategies based on quartiles of the workday TWA, but were related to pattern clusters and occupation. The linear discriminant function produced very low misclassification error rates for the cluster grouping strategy (10%) compared to occupation (50%) and TWA quartile (69%) grouping strategies. Significant differences in the exposure patterns occurring between clusters and between occupational groups were observed, indicating that at least one of the spectral estimates in two of the groups were significantly different. However, patterns clusters produced the greatest contrast in exposure patterns of all grouping strategies, explaining 99 percent of the total variation compared to 58 percent of the total variation by occupation.

Electromagnetic Fields↗
PubMed2005

In situ 1H NMR study on the trioctylphosphine oxide capping of colloidal InP nanocrystals.

We used trioctylphosphine oxide (TOPO) capped colloidal InP nanocrystals (Q-InP|TOPO) to explore the potential of solution 1H NMR spectroscopy in studying in situ the capping and capping exchange of sterically stabilized colloidal nanocrystals. The spectrum of Q-InP|TOPO shows resonances of free TOPO, superimposed on broadened spectral features. The latter were assigned to TOPO adsorbed at Q-InP by means of pulsed field gradient diffusion NMR and 1H-13C HSQC spectroscopy. The diffusion coefficient of Q-InP|TOPO nanocrystals was inferred from the decay of the adsorbed TOPO NMR signal. The corresponding hydrodynamic diameter correlates well with the diameter of Q-InP. By using the resolved methyl resonance of adsorbed TOPO, the packing density of TOPO at the InP surface can be estimated. Spectral hole burning was used to demonstrate explicitly that the adsorbed TOPO resonances are heterogeneously broadened. Exchange of the TOPO capping by pyridine was demonstrated by the disappearance of the resonances for adsorbed TOPO and the appearance of pyridine resonances in the 1H NMR spectrum. These results show that solution NMR spectroscopy should be considered a powerful technique for the in situ study of the capping of sterically stabilized colloidal nanocrystals.

Journal Article↗
PubMed1989

A real-time autoregressive spectrum analyzer for Doppler ultrasound signals.

A system based on a digital signal processor and a microcomputer has been programmed to estimate the maximum entropy autoregressive (AR) power spectrum of ultrasonic Doppler shift signals and display the results in the form of a sonogram in real-time on a computer screen. The system, which is based on a TMS 320C25 digital signal processor chip, calculates spectra with 128 frequency components from 64 samples of the Doppler signal. The samples are collected at a programmable rate of up to 40.96 kHz, and the computation of each spectrum takes typically 3.2 ms. The feasibility of on-line AR spectral estimation makes this type of analysis an attractive alternative to the more conventional fast Fourier transform approach to the analysis of Doppler ultrasound signals.

Analog-Digital Conversion↗
PubMed1995

Day-to-day variability of cardiac autonomic regulation parameters in normal subjects.

We examined the reproducibility of day-to-day variability in cardiovascular autonomic nervous function parameters (classical clinical tests and domain analysis of heart rate variability) in four healthy men during a period of 1 working week. The results did not show any significant difference in any of the parameters over the five repeated measurements. The maximum-minimum difference as percentage of the mean was under 15% for expiration to inspiration (E/I) ratio, Valsalva ratio, tachycardia ratio, 30/15 ratio, acceleration index and brake index; about 45% for baroreflex sensitivity for systolic and diastolic blood pressure and for root mean square difference (RMSSD) of successive R-R intervals; about 65-85% for low and high frequency bands, total power and medium to high frequency ratio; and about 125% for medium frequency band. The intraclass correlation coefficient (ICC) values showed that the agreement for classical autonomic parameters (except for brake index) was good. ICC for RMSSD, baroreflex sensitivity for systolic blood pressure and the spectral estimates of heart rate variation were less good. Coefficient of variation (CV) was 4% for E/I ratio, 2% for 30/15 ratio, 6% for Valsalva, 3% for tachycardia ratio, 4% for acceleration index and 5% for brake index. CV for baroreflex sensitivity and for RMSSD was about 20%. It is concluded that the variation in baroreflex sensitivity is clearly larger than in the classical autonomic nervous function parameters. One-minute fixed pace breathing period seems to be too short to allow reproducible measurement of RMSSD and the spectral parameters of heart rate variation. Learning effect could be excluded.

Adult↗