Phase spectral estimates of electrical activity of the rat brain during the estrus cycle.
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Action Potentials↗
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Spectral estimation of cardiovascular sounds.
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Adult↗
Spectral analysis of heart valve sound for detection of prosthetic heart valve diseases.
The spectral analysis of heart valve sound is a noninvasive diagnostic method known to be useful in evaluating the state of the heart valve function. This may provide early detection of valve calcification, thrombus or destruction, since previous studies have shown that the dominant frequency peak moved to a high frequency area when natural heart valve leaflets were calcified, stiffened or destroyed. However, it is important for a heart valve sound diagnostic system to find a proper spectral analysis method on phonocardiography. Until now, conventional frequency analyses such as the Fourier transform or autoregressive spectral estimation technique have been used to estimate spectral components of a phonocardiogram, but they are inappropriate because the signal frequency is assumed to remain constant during the transform interval. To overcome this problem, in this study, FOS (Fast Orthogonal Search) & MUSIC (MUltiple SIgnal Classification), which both appeared suitable for the analysis of biological data, were applied to prosthetic heart valve sound as the new heart valve sound spectral analysis methods. Five subjects with normally functioning mechanical heart valves and a patient with a malfunctioning one were selected to collect the heart valve sound signals. As a result, the second dominant peak frequency proved to be important along with the first dominant peak frequency in identifying the valve function. This study showed that the new heart valve sound spectral analysis method presented in this paper may be an effective method in heart valve sound analysis. Further study using this system in a large population of patients will aid in providing a diagnostic method in the early detection of valve failure.
Female↗
Real-time system for robust spectral parameter estimation in Doppler signal analysis.
In assessing the level of stenosis in extracranial Doppler analysis, spectral analysis has until now been used qualitatively, for the most part. Owing to the many variables affecting the measurements (mainly noise level and instrument setting made subjectively by the operator), the reliability of the inferences on the degree of stenosis is not clearly definable. Under such conditions the need arises for algorithms and systems that can estimate spectral parameters with a higher degree of accuracy, to verify whether reliable inferences can indeed by made or if this technique is only a qualitative one. In the paper a real-time spectral analysis system is described. The system relies on a new spectral estimation algorithm which gives estimates with good robustness with respect to noise. Moreover, a clear measurement procedure which eliminates the many subjective factors affecting the estimates has also been proposed and used. The system has been evaluated with simulated signals and in clinical trials and has shown better performance than the commonly used commercial analysers.
Adult↗
A modeling cross-spectral analysis technique based on the Prony Spectral Line Estimator (PSLE).
The Cross-Prony Spectral Line Estimator (XPSLE) is proposed for spectral comparison of short data records. Basic theory is discussed. The XPSLE method is tested on pairs of synthetic data records and is shown to be sensitive to disparity of spectral content. Application to analysis of arteriolar vasomotion is discussed.
Algorithms↗
The Prony spectral line estimation (PSLE) method for the analysis of vascular oscillations.
The Prony spectral line estimation (PSLE) technique is implemented and tested on data consisting of sinusoids mixed with Gaussian noise and on recordings of oscillatory diameter changes (vasomotion) of arterioles. It is concluded that the PSLE method is well suited for the spectrum analysis of short oscillatory diameter records.
Animals↗
Autoregressive spectral models of heart rate variability. Practical issues.
Autoregressive time series model-based spectral estimates of heart period sequences can provide a parsimonious and visually attractive representation of the dynamics of interbeat intervals. While a corollary to Wold's decomposition theorem implies that the discrete Fourier periodogram spectral estimate and the autoregressive spectral estimate converge asymptotically, there are practical differences between the two approaches when applied to short blocks of data. Autoregressive spectra can achieve good frequency resolution and excellent statistical stability on short segments of heart period data of sinus origin. However, the order of the autoregressive model (number of free parameters to be estimated) must be explicitly chosen, a decision that influences the trade-off of frequency resolution with statistical stability. Akaike's Information Criterion (AIC), an information-theoretic rule for picking the optimum order, is sensitive to the aggregate amount of data in the analysis. Thus, the best model order for estimating the spectrum of a 4-minute segment of data will generally be lower than the best order for estimating an hourly spectrum based on averaging 15 4-minute spectra. A major advantage of the autoregressive model approach to spectral analysis is the ease with which it can be extended to handle messy data frequently seen in heart rate variability studies. A number of autoregressive-based robust-resistant techniques are available for the analysis of heart period sequences that contain a high volume of nonsinus and other unusual beats intervals. A theoretically satisfying framework is also available for spectral analysis of unevenly sampled data and missing data.
Fourier Analysis↗
In vivo NMR spectral parameter estimation: a comparison between time and frequency domain methods.
We have compared various methods of in vivo NMR spectral parameter estimation, namely a nonlinear fit of the free induction decay signal in the time domain (NLTD), a nonlinear fit of the fast Fourier transform of the FID data in the frequency domain using either a continuous Lorentzian model (NLLM) or a Fourier-sampled model (NLFM), and a time-domain linear prediction method using singular value decomposition (LPSVD). Monte Carlo simulations of 31P and 13C in vivo experiments were used to assess the bias and statistical uncertainties of spectral parameters obtained with each method. In the 31P case, all methods appear to be equivalent except the LPSVD method that led to significantly biased peak amplitudes (up to 28%). In the 13C case, the only methods able to recover the glycogen peak were the NLTD method and its equivalent in the frequency domain (NLFM). In both the 31P and the 13C cases simulations demonstrated that 256 data points were sufficient. These results demonstrate the feasibility and the robustness of a nonlinear fit of the FID data in the time domain, and we illustrate this on 31P and 13C data sets obtained in humans.
Algorithms↗
Spectral parameter estimation by an iterative quadratic maximum likelihood method.
An iterative quadratic maximum likelihood (IQML) method is applied to spectral parameter estimation of 1D NMR data. A careful comparison of the linear prediction (LP) method based on the singular value decomposition, the total least squares (TLS) method, and IQML has clearly demonstrated that IQML is superior to both the LP and TLS methods in terms of the accuracy and bias of the estimation. The superiority of the IQML method lies in the fact that constraints on the NMR signal can easily be incorporated into the iterative process. The iterative quadratic maximum likelihood method can be used to analyze NMR data directly or to provide a starting point for further data refinement.
Algorithms↗
Power spectral strain estimators in elastography.
Elastography can produce quality strain images in vitro and in vivo. Standard elastography uses a coherent cross-correlation technique to estimate tissue displacement and tissue strain using a subsequent gradient operator. Although coherent estimation methods generally have the advantage of being highly accurate and precise, even relatively small undesired motions are likely to cause enough signal decorrelation to produce significant degradation of the elastogram. For elastography to become more universally practical in such applications as hand-held, intravascular and abdominal imaging, the limitations associated with coherent strain estimation methods that require tissue and system stability, must be overcome. In this paper, we propose the use of a spectral-shift method that uses a centroid shift estimate to measure local strain directly. Furthermore, we also show theoretically that a spectral bandwidth method can also provide a direct strain estimation. We demonstrate that strain estimation using the spectral-shift technique is moderately less precise, but far more robust than the cross-correlation method. A theoretical analysis, simulations and experimental results are used to illustrate the properties associated with this method.
Elasticity↗
Application of time series spectral analysis theory: analysis of cardiovascular variability signals.
The paper focuses on the most important application problems commonly encountered in spectral analysis of short-term (less than 10 min) recordings of cardiovascular variability signals (CVSs), critically analysing the different approaches to these problems presented in the literature and suggesting practical solutions based on sound theoretical and empirical considerations. The Blackman-Tukey (BT) and Burg methods have been selected as the most representative of classical and AR spectral estimators, respectively. For realistic simulations, 'synthetic' CVSs are generated as AR processes whose parameters are estimated on corresponding time series of normal, post-myocardial infarction and congestive heart failure subjects. The problem of resolution of spectral estimates is addressed, and an empirical method is proposed for model order selection in AR estimation. The issue of the understandability and interpretability of spectral shapes is discussed. The problem of non-stationarity and removing trends is dealt with. The important issue of identification and estimation of spectral components is discussed, and the main advantages and drawbacks of spectral decomposition algorithms are critically evaluated.
Heart Diseases↗
Multivariate time-variant identification of cardiovascular variability signals: a beat-to-beat spectral parameter estimation in vasovagal syncope.
In this paper a bivariate, time-variant model able to continuously measure the mutual interactions between heart rate and systolic blood pressure variability signals is presented. A recursive identification of the model parameters makes it possible to estimate, on a beat-to-beat basis, spectral low-frequency (LF) and high-frequency (HF) power, (LF/HF ratio) and cross-spectral (coherence and phase relationships between spectral peaks) indexes during nonstationary events. These indexes can be helpful in: 1) physiological study of autonomic nervous system mechanisms of cardiovascular control and 2) quantification and clinical evaluation of the neural and mechanical links between the two signals. In addition, an estimate of baroreceptive activation (alpha-gain) is continuously extracted. Before applying the model to cardiovascular signals, the reliability of the estimated parameters was tested on simulated signals. Subsequently, the model was applied to investigating vasovagal syncope episodes, aiming at the assessment of autonomic nervous system status and autonomic role in the dynamic phenomena which lead to syncope. The proposed model, which provides noninvasive beat-to-beat evaluation of the autonomic events, may be useful in the description of the syncopal episodes and in the comprehension of the complex physiological mechanisms of syncope.
Algorithms↗
Moments of the power spectral density estimated from samples of the autocorrelation function (a robust procedure for monitoring changes in the statistical properties of lengthy non-stationary time series such as the EEG.
Accurate estimates of the statistical moments of the power spectral density (PSD) are obtained without computing the Fourier transform of the associated time series. An innovative analytical procedure is derived which reduces the problem to that of summing a small number of weighted samples of the autocorrelation function (ACF). This result significantly reduces the computational requirements for generating meaningful PSD shape descriptors and thus is especially important in biomedical applications where the cost and effort of monitoring lengthy non-stationary time series is a serious practical limitation. In addition the procedure is robust and therefore can be rigorously applied to any stochastic process to estimate its fundamental statistical properties.
Adult↗
Time-frequency methods applied to muscle fatigue assessment during dynamic contractions.
This paper discusses the assessment of the electrical manifestations of muscle fatigue during dynamic contractions. In the past, the study of muscle fatigue was restricted to isometric constant force contractions because, in this contraction paradigm, the myoelectric signal may be considered as wide sense stationary over epochs lasting up to two or three seconds, and hence classic spectral estimation techniques may be applied. Recently, the availability of spectral estimation techniques specifically designed for nonstationary signal analysis made it possible to extend the employment of muscle fatigue assessment to cyclic dynamic contractions, thus increasing noticeably its possible clinical applications. After presenting the basics of time-frequency distributions, we introduce instantaneous spectral parameters well suited to tracking spectral changes due to muscle fatigue, discuss the issues of quasi-stationarity and quasi-cyclostationarity, and present different strategies of signal analysis to be utilized with cyclic dynamic contractions. We present preliminary results obtained by analyzing data collected from paraspinal muscles during repetitive lift movements, from the first dorsal interosseus during abduction-adduction movements of the index finger, and from knee flexors and extensors during isokinetic exercise. In conclusion, data herein reported demonstrate that the described techniques allow for evidencing the electrical manifestations of muscle fatigue in different paradigms of cyclic dynamic contractions. We believe that the extension of the objective assessment of the electrical manifestations of muscle fatigue from static to dynamic contractions may increase considerably the interest of researchers and clinicians and open new application fields, as ergonomics and sports medicine.
Electromyography↗
Mean-scatterer spacing estimates with spectral correlation.
An ultrasonic backscattered signal from material comprised of quasiperiodic scatterers exhibit redundancy over both its phase and magnitude spectra. This paper addresses the problem of estimating mean-scatterer spacing from the backscattered ultrasound signal using spectral redundancy characterized by the spectral autocorrelation (SAC) function. Mean-scatterer spacing estimates are compared for techniques that use the cepstrum and the SAC function. A -scan models consist of a collection of regular scatterers with Gamma distributed spacings embedded in diffuse scatterers with uniform distributed spacings. The model accounts for attenuation by convolving the frequency dependent scattering centers with a time-varying system response. Simulation results indicate that SAC-based estimates converge more reliably over smaller amounts of data than cepstrum-based estimates. A major reason for the performance advantage is the use of phase information by the SAC function, while the cepstrum uses a phaseless power spectral density that is directly affected by the system response and the presence of diffuse scattering (speckle). An example of estimating the mean-scatterer spacing in liver tissue also is presented.
Culture Techniques↗
Heart rate variability in the horse by ambulatory monitoring.
Using a microprocessor controlled Ambulatory Monitoring System (AMS) developed by one of us (LvD), we have been studying the changes in and control of heart rate in the resting horse. The system provides us with InterBeat Intervals (IBI in milliseconds), motion sensing, and a time domain measure (mean successive differences: MSD) of heart rate variability for periods up to 72 hours. Thoracic impedance is also available but parameters for the equine chest are not currently available. The system is completely noninvasive, small, and carried on a surcingle worn by the subject. The equine subject is confined to a stall in our teaching hospital but not otherwise restrained. Recording is virtually unobtrusive. Ten horses (judged to be clinically normal) were used in this preliminary study. After collection, the data were "offloaded" to a laptop computer for additional analysis. The electrocardiogram could be recorded on each of the ten animals. Complete data, suitable for spectral analysis, were obtained from four of the animals. Spectral estimates were calculated on periods of varying lengths (3-5 minutes) with more stable spectral estimates associated with longer recording periods. Results indicated the preponderance of parasympathetic control in equid heart rate. These results provide support for the utility of this method for the study of heart rate variability in the freely behaving horse.
Animals↗
Statistical bias and variance in blood flow estimation by spectral analysis of Doppler signals.
The stochastic nature of the Doppler signal is discussed as a source of variability and bias in estimation of mean blood velocity and flow performed using the Discrete or Fast-Fourier Transform. The estimators studied are those appropriate when the ultrasound beam is assumed to be wide enough to insonate the cross-section of the vessel uniformly, and assumed to be narrow enough to insonate only a diameter. Approximate expressions are derived theoretically for the biases and variances of these statistics when the Doppler power spectrum is uniform. For more complex spectra numerical evaluation is carried out by computer. Results for the double-sided spectra obtained from mixed flow are inferred from the single-sided cases. Typically, an estimate of instantaneous mean velocity has very little bias added (< 1%) but might have a standard error of approximately 10% of its mean value, and an estimate of flow in one cardiac cycle a standard error of approximately 1% of its mean value.
Bias↗
Time-frequency analysis of postural sway.
Postural sway during quiet stance has been used to characterize the postural control system. Most studies have used center of pressure (COP) measurements and have assumed stationarity, however, recent research has indicated that COP is not stationary. The purpose of this study is to introduce and demonstrate a nonstationary spectral estimation technique to examine the time-varying nature of postural sway. Data from two experiments were used to verify the usefulness of the spectral estimator for the analysis of COP. The first data set contains COP recorded from normal subjects swaying about their ankles in response to a metronome as it was gradually changed from 2 to 1 Hz. The time-frequency distribution reveals time-varying spectral changes corresponding to frequency changes made by the subjects. The second set consists of COP from normal subjects and vestibularly impaired patients standing quietly on a force plate with eyes closed for 100 s. The time-frequency distributions for the COP were estimated for both sets of data. The COP's appear to be nonstationary with the energies at a given frequency modulating through time.
Computer Simulation↗