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PubMed2003

A method for estimating the CTF in electron microscopy based on ARMA models and parameter adjustment.

In this work, a powerful parametric spectral estimation technique, 2D-auto regressive moving average modeling (ARMA), has been applied to contrast transfer function (CTF) detection in electron microscopy. Parametric techniques such as auto regressive (AR) and ARMA models allow a more exact determination of the CTF than traditional methods based only on the Fourier transform of the complete image or parts of it and performing some average (periodogram averaging). Previous works revealed that AR models can be used to improve CTF estimation and the detection of its zeros. ARMA models reduce the model order and the computing time, and more interestingly, achieve increased accuracy. ARMA models are generated from electron microscopy (EM) images, and then a stepwise search algorithm is used to fit all the parameters of a theoretical CTF model in the ARMA model previously calculated. Furthermore, this adjustment is truly two-dimensional, allowing astigmatic images to be properly treated. Finally, an individual CTF can be assigned to every point of the micrograph, by means of an interpolation at the functional level, provided that a CTF has been estimated in each one of a set of local areas. The user need only know a few a priori parameters of the experimental conditions of his micrographs, for turning this technique into an automatic and very powerful tool for CTF determination, prior to CTF correction in 3D-EM. The programs developed for the above tasks have been integrated into the X-Windows-based Microscopy Image Processing Package (Xmipp) software package, and are fully accessible at www.biocomp.cnb.uam.es.

Algorithms↗
PubMed1997

Heart rate variability spectral indices for haemodynamic classification of haemodialysis patients.

The usefulness of spectral indices extracted from the heart rate variability (HRV) in discriminating between hypotension-prone and hypotension-resistant haemodialysis patients was investigated. In 30 patients, classified as hypotension resistant (stable group) or hypotension prone (unstable group), beat-to-beat heart period was measured during haemodialysis sessions terminated without collapses. HRV was analysed in the frequency domain combining classic autoregressive spectral estimation with two eigen decomposition-based techniques: the reduced rank approximation (RRA) of the autocorrelation matrix and the Pisarenko harmonic decomposition (PHD). Five spectral indices were obtained: the ratio between the powers in the LF and HF bands (LF/HF), the same ratio calculated after application of RRA (LF/HFRRA), the frequency of the main oscillatory component of HRV estimated through PHD with a decomposition order equal to 1 (F1) and equal to 2 (F2) and the difference between the frequencies of the two oscillatory components resolved in the latter cas (Fd). The performances of these indices in discriminating between the two groups of patients were evaluated estimating the misclassification probability (Pm) of a Bayesian quadratic classifier. The HRV spectral pattern was markedly different: in the stable patients power was mainly in the low-frequency band, whereas in the unstable group it was mainly in the high-frequency band. The frequency of the main oscillatory component was significantly greater in the unstable group than in the stable one. Spectral indices displayed good discrimination power, increasing with the length of the dialysis interval. Best performances were achieved by LF/HFRRA both over short dialysis periods (Pm approximately 12% over 20 min intervals) and over longer periods (Pm = 3.3% over 160 min); similar results were obtained with Fd over short periods and LF/HF over long periods. Spectral HRV indices demonstrate, therefore, a diagnostic value in discriminating between hypotension-resistant and hypotension-prone patients.

Bayes Theorem↗
PubMed2001

Distinguishing subtypes of temporal lobe epilepsy with background hippocampal activity.

PURPOSE: Two subtypes of temporal lobe epilepsy (TLE) can be defined through clinical observations and analysis of hippocampal tissue resected during surgical procedures for intractable TLE: (a) mesial temporal sclerosis (MTS), which is characterized by extensive changes to the hippocampus and good surgical outcome; and (b) paradoxical temporal lobe epilepsy (PTLE), which is characterized by minimal cell loss and comparatively poorer surgical outcome. Patients in both subtypes have seizures that appear to begin in the medial temporal lobe, but documented differences in substrate and outcome between these subtypes has defined a need to distinguish MTS and PTLE patients before surgery. This report describes a retrospective study to investigate the feasibility of doing so during intracranial monitoring. METHODS: Background EEG epochs, 5 min in duration, were recorded from the anterior hippocampus in 14 (10 MTS and four PTLE) patients with consistent localization of seizure onset to medial temporal structures. The power spectral density (PSD) of the EEG epochs was calculated by a Fourier spectral estimator, and the total signal power and power of the delta, theta, alpha, beta, and gamma frequency bands were submitted to group-to-group comparison. RESULTS: Spectral peaks were observed in the delta band in all PSD estimates and in the theta band in nine of 14 (seven MTS, two PTLE) estimates. The MTS and PTLE subtypes could be distinguished by the total signal power and delta band power. These power measurements were greater in the PTLE subtype. CONCLUSIONS: Both delta and theta spectral components are present in hippocampal background EEGs recorded from patients with TLE. The results indicate that group differences exist in spectral measures of background hippocampal signals recorded from MTS and PTLE subtypes. This suggests both that substrate differences in cellular composition and connectivity are reflected in hippocampal background EEGs and that spectral measurements of these signals may hold promise for tests to identify the group membership of individual patients.

Adult↗
PubMed2002

Doppler angle estimation using AR modeling.

The transit time spectrum broadening effect has long been explored for Doppler angle estimation. Given acoustic beam geometry, the Doppler angle can be derived based on the mean Doppler frequency and the Doppler bandwidth. Spectral estimators based on the fast Fourier transform (FFT) are typically used. One problem with this approach is that a long data acquisition time is required to achieve adequate spectral resolution, with typically 32-128 flow samples being needed. This makes the method unsuitable for real-time two-dimensional Doppler imaging. This paper proposes using an autoregressive (AR) model to obtain the Doppler spectrum using a small number (e.g., eight) of flow samples. The flow samples are properly selected, then extrapolated to ensure adequate spectral resolution. Because only a small number of samples are used, the data acquisition time is significantly reduced and real-time, two-dimensional Doppler angle estimation becomes feasible. The approach was evaluated using both simulated and experimental data. Flows with various degrees of velocity gradient were simulated, with the Doppler angle ranging from 20 degrees to 75 degrees. The results indicate that the AR method generally provided accurate Doppler bandwidth estimates. In addition, the AR method outperformed the FFT method at smaller Doppler angles. The experimental data for Doppler angles, ranging from 33 degrees to 72 degrees, showed that the AR method using only eight flow samples had an average estimation error of 3.6 degrees, which compares favorably to the average error of 4.7 degrees for the FFT method using 64 flow samples. Because accurate estimates can be obtained using a small number of flow samples, it is concluded that real-time, two-dimensional estimation of the Doppler angle over a wide range of angles is possible using the AR method.

Algorithms↗
PubMed1989

A real-time system for the spectral analysis of the EEG.

We have developed an inexpensive and portable system for processing multiple channels of electroencephalograms (EEG) in real-time to assist the electroencephalographer in identifying subtle changes in these data, particularly in the interpretation of long records. Up to 16 channels of data are spectrally decomposed with a selection of bandwidth, windows, scaling methods, epoch averaging, and smoothing options available. The resulting power spectral estimate may be displayed or printed in a variety of formats, including color encoding of selectable spectral bands. Both the sampled EEG and its spectra may be stored for off-line reprocessing, for archiving, or for statistical analysis in the time or frequency domains.

Analog-Digital Conversion↗
PubMed1998

Spectral analysis methods for neurological signals.

This paper reviews some novel spectral analysis techniques that are useful for neurological signals in general and EEG signals in particular. First, some drawbacks and limitations of the commonly used Fast Fourier transforms (FFTs) are presented, and then alternative algorithms are outlined. An auto-regressive (AR) modeling based spectral estimation procedure is presented to overcome the problems of lower resolution and 'leakage' effects inherent in the FFT algorithm. For signals which are transient in nature or rapidly time-varying, two alternative algorithms are presented. The first is an adaptive AR parameter estimation algorithm and the second is a wavelet based time-frequency representation algorithm. Finally, a Spectral Distance measure and the Itakura distance measure are presented to quantify the differences between the spectra of two signals in a succinct manner. The application and performance of all the algorithms is illustrated using electroencephalograms (EEGs) recorded in animals during hypoxic asphyxic injury to brain.

Algorithms↗
PubMed1991

Estimation of time delay between EEG signals for epileptic focus localization: statistical error considerations.

A theoretical analysis of the variance for the time delay estimate between two EEG signals, obtained via the phase spectrum method, is presented. Explicit theoretical formulae for the variance are obtained and compared via simulations to experimentally derived results for estimate variability. The variance of the time delay estimate is inversely proportional to the frequency range of interest, to the number of data segments utilized for spectral estimation, and to the coherence between the two EEG signals. The simulations indicate that the formulae can be used even with non-gaussian and relatively narrow-band EEG-like data. A minimum-variance estimate for the time delay is also presented.

Computer Simulation↗
PubMed1996

Neuromagnetic study of movement-related changes in rhythmic brain activity.

Neuromagnetic fields from the left cerebral hemisphere of five healthy, right-handed subjects were investigated in a typical Bereitschaftspotential paradigm consisting of self-paced voluntary movement of the right index finger. To assess movement-related spectral changes of the spontaneous magnetoencephalogram. latency-dependent short-time spectra were obtained by Fourier analysis for each single trial. The number of trials in which the spectral estimate for a certain frequency and latency deviated from reference values was then transformed into a probabilistic relative power measure. A spectral power depression around 20 Hz was observed starting about 2.5 s before movement onset, followed by elevated power in the 20-35 Hz range starting about 500 ms after movement onset. Generally, the power increase differed from the prior depression in both spectrum and topography, suggesting different generating processes rather than just a 'rebound' effect of the idling rhythm generator. The time course and topography of spectral power changes are discussed in relation to the corresponding properties of the movement-related neuromagnetic fields (readiness field, motor field, and movement-evoked field I).

Adult↗
PubMed2000

A chemometric approach to the estimation of the absorption spectra of dye probe merocyanine 540 in aqueous and phospholipid environments.

Merocyanine 540 (MC540) is a widely used dye probe for membranous environments. However, fundamental knowledge of the spectral features of this dye in aqueous and hydrophobic environments is still lacking. Such knowledge is important because biomembranes involve a hydrophobic environment surrounded by a hydrophilic environment. Because many investigations so far have been performed based on indistinct spectral estimations, the interpretation of the data obtained using this dye as a fluorescent transmembrane probe remains controversial. In order to determine the exact spectra in both aqueous and hydrophobic environments, we adopted principal factor analysis (PFA), a method of multivariate analysis. The PFA method can also determine the number of molecular species present in the reaction mixture, which is three in pure water and two in phospholipid suspension. Two of the species in both water and phospholipid suspension were the monomer and dimer. The third species in water was the trimer, but its amount was so small at 10 microM MC540 solution that the spectral data in water can be approximated neglecting this molecular species. The monomer spectrum changed its form markedly with a bathochromic shift when transferred from the water to phospholipid environment, whereas the dimer remained similar in its shape except for a remarkable red shift. In water, the dissociation constants, K(1) and K(2), for the assumed stacking-model reactions, M+M <--> M(2) and M+M(2) <--> M(3), were 3.1 x 10(-4) M and 5.7 x 10(-4) M, respectively. In the phospholipid environment, the dissociation constant K* for the assumed stacking-model reaction, M(*)+M(*) <--> *M(2), was 1.9x10(-5)M. The fluorescent intensities of MC540 were also measured in both water and phospholipid environments. A comparison based on the absorption and fluorescence spectra suggested that the temporal increase in the amount of the monomer on the excitable membrane contributes to the fluorescent intensity change observed in the transmembrane potential change.

Fluorescent Dyes↗
PubMed1987

Secretory pattern of canine growth hormone.

Our aim was to define the secretory pattern of growth hormone (GH) under basal conditions in fasted, conscious, male dogs accustomed to handling. Blood samples were withdrawn from a cephalic vein at 15-min intervals. In this way, any ultradian rhythms, if present, could be detected within the frequency range of 0.042-2 cycles/h. In addition, samples were drawn at either 1- or 2.5-min intervals for 2.5 or 5 h to determine whether frequency components greater than 2 cycles/h were present. GH was measured by radioimmunoassay and the raw data were submitted to time series analysis employing power spectral estimation by means of fast Fourier transformation techniques. Peak plasma levels were up to 12 times higher than the baseline concentration of approximately 1 ng/ml. Spectral analysis revealed an endogenous frequency of 0.22 cycles/h, i.e., a periodicity of 4.5 h/cycle. The results indicate that under basal conditions the secretory bursts of canine GH are limited to one peak every 4.5 h.

Animals↗
PubMed1999

Instantaneous postural stability characterization using time-frequency analysis.

Postural stability assessment is critical to a more accurate understanding of sway and balance control. The center of pressure (COP) metric has been shown to be a suitable output measure for time and frequency analysis. However, the center of pressure is a non-stationary signal. Standard time and frequency analysis methods may not be adequate for monitoring the dynamic changes in the center of pressure signal. In this study a time-frequency method, based on data-adaptive evolutionary spectral estimation, is applied to monitor the dynamic changes of the center of pressure in a non-stationary environment. Metrics including the instantaneous mean frequency (IMF), instantaneous spectral bandwidth (ISB), and instantaneous average power (IAP) are analyzed to characterize the center of pressure signal in both the anterior-posterior (AP) and the medial-lateral (ML) planes. Within the confines of this study, the IMF was found to be inversely proportional to IAP. The inverse proportionality factors were calculated in both eyes-open and eyes-closed trials during upright quiet standing. These findings suggest that the time-frequency analysis provides instantaneous metrics which describe the amplitude changes and frequency shift of the center of pressure under a variety of environmental conditions, thus providing a more reliable quantification of postural control.

Adult↗
PubMed1987

Strategies for the representation of a tone in background noise in the temporal aspects of the discharge patterns of auditory-nerve fibers.

The responses of populations of auditory-nerve fibers to both a 1.0-kHz tone, and 1.0-kHz tone in broadband noise, have been measured. Period histograms were generated from fiber spike trains and discrete Fourier transforms (DFTs) with a resolution of 125 Hz were computed from each histogram. Sample mean and sample variance statistics were generated for period histograms of response and for temporal response measures derived from discrete Fourier transforms. It is demonstrated how the statistical properties of auditory-nerve fiber response determine the strategy for the estimation and discrimination of particular stimulus components. When the tone is presented alone, the entire population of auditory-nerve fibers provides statistically reliable estimates of the 1.0-kHz tone. Upon addition of the broadband noise stimulus only those units with characteristic frequencies which are close in frequency to the 1.0-kHz stimulus provide spectral estimates which have high signal-to-noise ratios (mean-squared-to-variance ratios). Estimates of the 1.0-kHz-tone stimulus derived from auditory-nerve fibers with characteristic frequencies which are far from the 1.0-kHz stimulus are statistically unreliable. Based on the responses of the population of auditory-nerve fibers, the strategy for estimating the 1.0-kHz-tone stimulus is to derive estimates of the 1.0-kHz stimulus from the subpopulation of neurons with characteristic frequencies close to the 1.0-kHz stimulus. It is concluded that neurons which are tuned close to 1.0-kHz provide the central nervous system (CNS) with the most salient information about the 1.0-kHz stimulus in the presence of the broadband background.

Analysis of Variance↗
PubMedDate not supplied

Characteristics of fricatives and sentence duration after cochlear implantation.

Ten postlingually deafened patients (5 male, 5 female) were examined after cochlear implantation to measure improvements in their quality of speech. Parameters such as the spectral maximum of fricatives and the duration of utterances were analysed in speech recordings taken at regular intervals after implantation. The speech samples were recorded in an audiological chamber. Parameters were analysed using ST(x) (S-Tools Software). Frequency analyses based on the fast Fourier transform and spectral estimation methods, as well as fundamental frequency and formant extraction (cepstrum, LPC = linear prediction coding) and digital filter implementations were prepared. The results indicate a tendency towards improvement in the spectral maximum of the fricatives and affricates and a shortening of the duration of the fricative parts in affricates and of sentences in nearly all our subjects. These results showed the restored auditory feedback produced by cochlear implantation to have a favourable effect on speech production.

Adolescent↗
PubMed1993

Low-frequency respiratory mechanics using ventilator-driven forced oscillations.

We evaluated the potential for using a fast Fourier transform (FFT) analysis applied to a standard ventilator waveform to estimate (< 2 Hz) frequency dependence of respiratory or lung resistance (R) and elastance (E). In four healthy humans we measured pressure and flow at the airway opening while applying sine wave forcing from 0.2 to 0.6 Hz at two tidal volumes (VT; 250 and 500 ml). We then applied a step inspiratory ventilator flow wave with relaxed expiration at the same VT and only 0.2 Hz. Step waveform data were also acquired from nine mechanically ventilated patients under intensive care unit conditions. Finally, we simultaneously measured total respiratory (rs), lung (L), and chest wall (cw) impedance data from two dogs (0.156-2 Hz) before and after severe pulmonary edema. Rrs and Ers were estimated by the FFT approach. Humans displayed a small frequency dependence in Rrs and Ers from 0.2 to 0.6 Hz, and both Rrs and Ers decreased at the higher VT. The spectral estimates of Rrs and Ers with the step ventilator wave were often qualitatively comparable to sine wave results below 0.6 Hz but became extremely erratic above the third harmonic. Conversely, in dogs the step wave produced reliable and stable estimates up to 2 Hz in all conditions. Nevertheless, Ecw and Ers still displayed clear and correlated oscillations with increasing frequency, whereas EL showed none. This suggests that nonlinear processes, most likely at the chest wall, contribute to periodic-like fluctuations in respiratory mechanical properties when estimated by applying FFT to a step ventilator wave. Moreover, in humans, but not dogs, a ventilator flow cycle contains insufficient signal energy beyond the third harmonic. We show that the amount of energy available at higher frequencies is largely governed by the mechanical time constant contributing to passive expiratory flow. In dogs the shorter time constant contributes to increased energy. In essence, the frequency content of the flow is subject dependent, and this is not a desirable situation for controlling the quality of the impedance spectra available from a standard ventilator wave.

Adult↗
PubMedDate not supplied

Time series versus Fourier transform methods for estimation of respiratory impedance spectra.

Most current techniques to estimate respiratory system mechanical input impedance spectra (Zrs) use digitally created (Fourier transform (FFT) based) random noise. Recent Zrs data reported from 0.1-4 Hz and above 32 Hz display sharper, more distinct spectral features. When expressing Zrs as a power spectral density (PSD), such features suggest the application of a time series spectral estimation approach. Here, a simulation study was performed to compare the quality of impedance (PSD and Zrs) estimates from the time series technique to those from the FFT approach in the presence of measurement noise. Random noise pressure and flow time domain sequences were simulated for two different networks, one which exhibits impedance features reported from 0.1-4 Hz and one which exhibits impedance features reported above 32 Hz. In the time series method, autoregressive (AR), moving average (MA), and autoregressive-moving average (ARMA) models were fit to the pressure and flow sequences separately. The estimated PSD and complex Zrs spectra were compared to the true spectra calculated from the models. Results show that the time series PSD estimates were reasonable even in the presence of additive measurement noise. Conversely, with additive noise, the time series estimates of the complex Zrs showed a negative real part which is physiologically inappropriate. This occurs because of the loss of phase information inherent to the time series approach. Regardless of measurement noise, the FFT estimates of impedance were always close to the true impedance and always superior to the time series estimates. We conclude that an accurate estimate of the PSD or complex Zrs spectra from digitally created FFT-based random noise is best obtained using the traditional FFT method.

Airway Resistance↗
PubMed1985

Reduced-order autoregressive modeling for center-frequency estimation.

The center frequency of a narrowband, discrete-time random process, such as a reflected ultrasound signal, is estimated from the parameter values of a reduced, second-order autoregressive (AR) model. This approach is proposed as a fast estimator that performs better than the zero-crossing count estimate for determining the center-frequency location. The parameter values are obtained through a linear prediction analysis on the correlated random process, which in this case is identical to the maximum entropy method for spectral estimation. The frequency of the maximum of the second-order model spectrum is determined from these parameters and is used as the center-frequency estimate. This estimate can be computed very efficiently, requiring only the estimates of the first three terms of the process autocorrelation function. The bias and variance properties of this estimator are determined for a random process having a Gaussian-shaped spectrum and compared to those of the ideal FM frequency discriminator, zero-crossing count estimator and a correlation estimator. It is found that the variance values for the reduced-order AR model center-frequency estimator lie between those for the ideal FM frequency discriminator and the zero-crossing count estimator.

Computers↗
PubMed1994

Real-time digital processing of Doppler ultrasound signals and calculation of flow parameters.

Vascular diseases and their complications are responsible for around 27% of deaths in Brazil. Doppler ultrasound is a non-invasive technique that has been used to study blood flow in intact blood vessels since Satomura first reported the potential of the technique in 1959. Because it is non-invasive it makes sequential studies and those in normals feasible. Whereas in contrast angiography only vessel anatomy is displayed, Doppler ultrasound produces dynamic information on blood-flow. It may be used to estimate flow-rates, to image regions of blood flow (colour Doppler), and to help in locating sites of arterial disease, thus complementing X-ray examinations. This paper describes a system based on a Digital Signal Processor for real-time spectrum analysis of Doppler ultrasound signals, real-time display of sonograms, and calculation and analysis of three parameters of clinical interest derived from the Doppler signal. The system comprises a TMS320C25 development board, which acquires the signal and performs spectrum analysis, and a microcomputer, which reads the spectral estimates, displays them as a sonogram in real-time and calculates a set of spectral parameters proposed in the literature. The system permits a maximum sampling frequency of 40.96 kHz, and in the sonogram, 80 power spectra per second (each with 128 frequency bins) are displayed. In a preliminary study, the stability of the haemodynamic parameters and their dependence on a user-defined threshold value is investigated.

Algorithms↗
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↗