Periodogram Method Research Articles

Sleep architecture and EEG power spectra in recently detoxified alcohol dependent patients.

Persistent sleep abnormalities during abstinence are a harbinger for relapse in patients with chronic alcohol dependence. The present study aimed to compare polysomnography (PSG) data between 'recently detoxified' patients with chronic alcohol dependence and healthy controls. Both conventional sleep architectural and power spectral analyses were conducted. Twenty subjects in each of the groups were enrolled. A 2 nights' sleep (first-habituation and second-experimental) PSG data was collected. Computer assisted scoring supplemented by manual method using the Rechtschaffen and Kales criteria were used for sleep staging. Twenty eight channels were used for the EEG recording. Spectral power across early NREM (Non-rapid-eye-movement), Slow Wave Sleep and REM was computed using the Welch's averaged periodogram method. Results on conventional sleep staging showed that patients had significantly lesser total sleep time, sleep efficiency and stage shifts and longer sleep onset latency; while duration of each NREM stages were significantly lower, and latency of stage 2 NREM was significantly longer in patients. After controlling for multiple comparisons, spectral power analysis revealed significant differences only during REM sleep and specifically in high frequency (beta and gamma) bands. Stating the mutually complementary role of conventional and spectral analyses of polysomnography EEG data, we conclude that sleep abnormalities are fairly evident in recently detoxified alcohol dependent patients.

Biomedical Signals Analysis Using the Empirical Mode Decomposition and Parametric and non Parametric Time-Frequency Techniques

In this paper, the Empirical mode decomposition (EMD) technique is used, in a first time, to separate the artifact from the biomedical signals. The biomedical signals treated are electroencephalogram (EEG) and Electrocardiogram (ECG) for normal and abnormal patients. Analysis of EEG and ECG signals is a challenging problem due to the fact that the signals are multi-component and very non-stationary. Due to the nature of these signals, the time-frequency analysis is an important tool for representing the evolution of the frequential components of these biomedical signals over time. The time-frequency techniques used are the parametric, Periodogram and Capon methods, and non-parametric Smoothed Pseudo Wigner-Ville method. As a first step of analysis, the EMD technique is applied to eliminate the artifact from these signals after that the parametric and non-parametric time-frequency techniques are processed to the results signals to present the non-stationary multicomponent of these signals in function of time. The results show that the EMD technique associated to the Periodogram method give a good localization of the transient abnormal ECG and the energies of the EEG signals as compared to others time-frequency methods associated to the EMD technique.

Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials.

Objectives: To compare quantitative EEG signal and test-retest reliability of medical grade and consumer EEG systems.Methods: Resting state EEG was acquired by two medical grade (B-Alert, Enobio) and two consumer (Muse, Mindwave) EEG systems in five healthy subjects during two study visits. EEG patterns, power spectral densities (PSDs) and test/retest reliability in eyes closed and eyes open conditions were compared across the four systems, focusing on Fp1, the only common electrode. Fp1 PSDs were obtained using Welch's modified periodogram method and averaged for the five subjects for each visit. The test/retest results were calculated as a ratio of Visit 1/Visit 2 Fp1 channel PSD at each 1 s epoch.Results: B-Alert, Enobio, and Mindwave Fp1 power spectra were similar. Muse showed a broadband increase in power spectra and the highest relative variation across test-retest acquisitions. Consumer systems were more prone to artifact due to eye blinks and muscle movement in the frontal region.Conclusions: EEG data can be successfully collected from all four systems tested. Although there was slightly more time required for application, medical systems offer clear advantages in data quality, reliability, and depth of analysis over the consumer systems.Significance: This evaluation provides evidence for informed selection of EEG systemsappropriate for clinical trials.

Periodicity for 50 yr of daily solar wind velocity

Daily mean velocity of solar wind that was surveyed near the Earth's orbit at about 1 au from 1963 November 27 to 2015 November 30 and issued by OMNIWeb is used to look into its periodicity through the Lomb-Scargle periodogram method. As the strongest periodical signal, the solar activity cycle of about 10.4 yr is found in high-velocity wind, but in low-velocity wind, the 9.17-yr cycle appears instead. The rotation cycle of about 27 d and its 1/2 and 1/3 harmonic periods are clearly detected in all-, low-and high-velocity wind, and at their periodograms, several individual periodical peaks appear very close to the peaks of these three periods. The annual period of about 1.07 yr is identified for both all-and low-velocity wind, but not for high-velocity wind after 1994. The 1.68-yr period occurs in all-and high-velocity wind, but does not appear in low-velocity wind. The period of about 2.42 yr appears just in the all-velocity wind after 1994, but its twofold period (about 4.83 yr) appears in both all-and high-velocity wind. The period of about 4.1 yr occurs in all-, low- and high-velocity wind. The possible origin of these periods is discussed.

Spectrum response estimation for deep-water floating platforms via retardation function representation

The key concept of spectrum response estimation with commercial software, such as the SESAM software tool, typically includes two main steps: finding a suitable loading spectrum and computing the response amplitude operators (RAOs) subjected to a frequency-specified wave component. In this paper, we propose a nontraditional spectrum response estimation method that uses a numerical representation of the retardation functions. Based on estimated added mass and damping matrices of the structure, we decompose and replace the convolution terms with a series of poles and corresponding residues in the Laplace domain. Then, we estimate the power density corresponding to each frequency component using the improved periodogram method. The advantage of this approach is that the frequency-dependent motion equations in the time domain can be transformed into the Laplace domain without requiring Laplace-domain expressions for the added mass and damping. To validate the proposed method, we use a numerical semi-submerged pontoon from the SESAM. The numerical results show that the responses of the proposed method match well with those obtained from the traditional method. Furthermore, the estimated spectrum also matches well, which indicates its potential application to deep-water floating structures.

Comparison of Spectral Estimation Methods for Rapidly Varying Currents Obtained by High-Frequency Radar

A comparative study of the periodogram method and high-resolution techniques (the autoregressive and multiple signal classification methods) for current mapping by a high-frequency (HF) surface wave radar is undertaken for the case of 66-s-long data. This analysis is extended from a previous study that used the commonly adopted 6–13-min coherent integration times. This reduction in the sample size will result in poor Doppler resolution and reduction in signal-to-noise ratio (SNR) for the conventional periodogram method. Two Bragg-peak identification methods for current estimation, the conventional centroid method and the symmetric-peak-sum (SPS) method, are examined in conjunction with each of the spectral estimation techniques. A weighted sum of the current estimates using the two Doppler shift identification methods is also recommended to provide a lower root mean square (RMS) difference. The weight is optimized using a genetic algorithm. Field data comparison with current measurements obtained from a current meter indicates that the high-resolution spectral estimation method is capable of providing the same RMS difference level for short and long time series, while the RMS difference for currents obtained from the periodogram method increases dramatically for short time series. Significant improvement in the current velocities retrieved from a short time series indicates the potential for measuring rapidly changing currents using the suggested technique.

Predictability of occupant presence and performance gap in building energy simulation

Occupant behavior is regarded as one of the major factors contributing to the discrepancy between simulation prediction and real energy use. Over the past several decades, occupants have been represented as fixed profiles of occupant presence in building energy simulation tools. Recently, stochastic models have been introduced to account for dynamic occupant presence. This stochastic approach is based on the premise that occupant presence can be described by empirical and probabilistic transition rules, e.g. Markov Chain.This paper presents evidence that occupant presence in some rooms and buildings follows a “random walk” pattern. In other words, occupant presence in certain types of buildings cannot be predicted stochastically. In this study, occupants’ presence in two laboratories and three reading rooms at two universities was monitored. The hypothesis of the random walk pattern was tested using the Normalized Cumulative Periodogram (NCP) method. Based on a series of six experiments, it is shown that each occupant’s presence in the five locations follows a random walk pattern. Three different occupant models (fixed ASHRAE model, Markov Chain model, and Random Walk model) were applied in EnergyPlus simulation runs. The adjusted R2 for three experiments between the fixed AHSRAE model and the random walk model, and between the Markov chain model and the random walk model are 0.54, 0.02, 0.01 and 0.86, 0.19, 0.41, respectively. This does not negate the need for the fixed ASHRAE model or the MC model. Rather, this signifies that, for a certain type of building, another occupant presence model should be introduced, e.g. the RW Model.

Qualitative Assessment of Certain Spectral Analysis Methods

Parametric methods of spectral analysis play an important role when solving the problems of primary radar data processing. The paper mainly focuses on comparing methods to obtain a spectrum by means of two criteria, i.e. computational complexity and resolution. The latter represents the most important characteristic of the algorithms used to process a spectrum of the reflected radar signal. The paper considers mathematical foundations of parametric methods, which use white noise as an excitation sequence, including the models: an autoregressive, moving average model and an autoregressive-moving average one. Reveals their differences from the classical approach to the spectral analysis represented by periodogram and correlogram methods. Emphasizes the greatest popularity of the autoregressive model due to linearity of dependencies used in it. Shows the main advantages and disadvantages of a number of autoregressive methods. Considers non-classical methods based on the analysis of eigenvalues. An experimental study of the spectral analysis methods in the MATLAB environment has been carried out for to compare them in terms of computational complexity and resolution. The simulation results with the input signal, represented by the superposition of harmonics on white noise, are shown. The character of the spectra peaks obtained and the order of each model, desirable for their resolution, are estimated. An analysis of the results obtained has shown that among the methods considered, those of based on the analysis of eigenvalues possess the best resolution and the greatest computational complexity. The autoregressive parametric methods have revealed similar properties. Classical methods are characterized by the least computational complexity and the least accuracy. A conclusion has been that the spectrum quality depends on the order of the model used and on the input signal noisiness. The results obtained can be used when solving the problems of primary radar data processing to provide a reasonable choice of spectral analysis methods based on resolution and computational complexity.

Applications of Cell-Ratio Constant False-Alarm Rate Method in Coherent Doppler Wind Lidar

A cell-ratio constant false-alarm rate (CR-CFAR) method for detecting the Doppler frequency shift is proposed to improve the accuracy of velocity measured by coherent Doppler wind lidar (CWL) in low signal-to-noise ratio (SNR) environments. The method analyzes the spectrum to solve issues of weak signal submergence in noise encountered in the widely used periodogram method. This characteristic is that the signal region slope is larger than the noise region slope in the frequency spectrum. We combined the ratio and CFAR to propose the CR-CFAR method. The peak area is discriminated from the spectrum using this method. By removing background noise, the peak signal is obtained along with the Doppler shift. To verify the CR-CFAR method, a campaign experiment using both CWL and a commercial Doppler lidar was performed in Hami, China (42°32′ N, 94°03′ E) during 1–7 June 2016. The results showed that the proposed method significantly improved the reliability of CWL data under low SNR conditions. The height—at which both horizontal wind speed correlativity and horizontal wind direction correlativity exceeded 0.99—increased by 65 m. The relative deviation of the horizontal wind speed at 120 m decreased from 40.37% to 11.04%. We used the CR-CFAR method to analyze continuous data. A greater number of wind field characteristics were obtained during observation compared to those obtained using the common wind field inversion method.

Source depth estimation of self-potential anomalies by spectral methods

Spectral analysis of the self-potential (SP) field for geometrically simple anomalous bodies is studied. In particular, three spectral techniques, i.e. Periodogram (PM), Multi Taper (MTM) and Maximum Entropy (MEM) methods, are proposed to derive the depth of the anomalous bodies. An extensive numerical analysis at varying the source parameters outlines that MEM is successful in determining the source depth with a percent error less than 5%. The application of the proposed spectral approach to the interpretation of field datasets has provided depth estimations of the SP anomaly sources in very good agreement with those obtained by other numerical methods.

Super-resolution estimation of cyclic arrival rates

Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process. The estimator is the super-resolution analogue to Shao (2010) and Shao and Lii [J. R. Stat. Soc. Ser. B. Stat. Methodol. 73 (2011) 99–122], which is a sum of $p$ sinusoids where $p$ and the amplitude and phase of each wave are not known and need to be estimated. This results in an interpretable yet flexible specification that is suitable for use in modelling as well as in high resolution simulations. Our estimation procedure sits in between classic periodogram methods and atomic/total variation norm thresholding. Through a novel use of window functions in the point process domain, our approach attains super-resolution without semidefinite programming. Under suitable conditions, finite sample guarantees can be derived for our procedure. These resolve some open questions and expand existing results in spectral estimation literature.

Eventogram: A Visual Representation of Main Events in Biomedical Signals.

Biomedical signals carry valuable physiological information and many researchers have difficulty interpreting and analyzing long-term, one-dimensional, quasi-periodic biomedical signals. Traditionally, biomedical signals are analyzed and visualized using periodogram, spectrogram, and wavelet methods. However, these methods do not offer an informative visualization of main events within the processed signal. This paper attempts to provide an event-related framework to overcome the drawbacks of the traditional visualization methods and describe the main events within the biomedical signal in terms of duration and morphology. Electrocardiogram and photoplethysmogram signals are used in the analysis to demonstrate the differences between the traditional visualization methods, and their performance is compared against the proposed method, referred to as the “eventogram” in this paper. The proposed method is based on two event-related moving averages that visualizes the main time-domain events in the processed biomedical signals. The traditional visualization methods were unable to find dominant events in processed signals while the eventogram was able to visualize dominant events in signals in terms of duration and morphology. Moreover, eventogram-based detection algorithms succeeded with detecting main events in different biomedical signals with a sensitivity and positive predictivity >95%. The output of the eventogram captured unique patterns and signatures of physiological events, which could be used to visualize and identify abnormal waveforms in any quasi-periodic signal.

Induction Motor Bearing Fault Analysis Using a Root-MUSIC Method

This paper describes a new diagnosis approach, the Root-Multiple Signal Classification (MUSIC) (RM) method, for identification of the progressive cracking in the bearing of induction motors. This approach has several advantages compared with the stator current spectral analysis using the conventional Periodogram method. Indeed, the main advantage of this approach is its very good frequency resolution for a very short acquisition time, something impossible to achieve with the conventional method. However, in order to reduce the computation time, which is the main drawback of the RM method, this method will be applied to only a specified frequency band; one that carries information about the sought fault. Experimental results show the effectiveness of the RM method on the reliability of the incipient bearing fault detection.

Self-mixing interferometry for rotational speed measurement of servo drives.

Self-mixing interferometry (SMI) is an efficient technique applied to measure distance, velocity, displacement, and vibration. In this work, a compact and low cost SMI is applied to measure the rotational speed of a servo drive up to 6000 RPM. The application of SMI to rotational speed measurement of servo drives instead of the usage of incremental encoders is proposed. The Doppler frequency is obtained via analysis on the power spectral density, which is estimated by the smoothing periodogram method based on the fast Fourier transformation. The signals are processed in MATLAB and LABVIEW, showing that the SMI can be applied to dynamic rotational speed measurement of servo drives. Results of experiments demonstrate that this system is implementable for rotational speed measurement over the whole range from 3 RPM to 6000 RPM. In addition, the system used to measure rotational speed can also accurately record changes in position without integrating the velocity.

Super-resolution Estimation of Cyclic Arrival Rates

Exploiting the fact that most arrival processes exhibit cyclic (though not necessarily periodic) behaviour, we propose a simple and computationally efficient procedure for estimating the intensity of a nonhomogeneous Poisson process. The estimator is interpretable because it is a simple sum of p sinusoids, although p and the frequency, amplitude, and phase of each wave are not known and need to be estimated. This results in a flexible specification that is suitable for use in modeling as well as in high resolution simulations. Our estimation procedure sits in between classic periodogram methods and atomic/total variation norm thresholding. A novel aspect of our approach is the use of window functions with fast decaying spectral tails, which we show improves frequency resolution as well as prevent strong frequency signals from masking weaker ones nearby. Interestingly the prolate spheriodal window that is usually considered optimal in signal processing is suboptimal for frequency recovery.Under suitable conditions, finite sample performance guarantees for our procedure can be derived that resolve open questions and expand existing results in spectral estimation literature. Preliminary simulations suggest that our procedure can potentially be extended to Cox processes which are used to model overdispersion, although a formal analysis is left for future research. We apply our procedure to arrivals data from an emergency department at an academic hospital in the United States to examine differences in arrival patterns by illness severity.

Prudence in estimating coherence between planetary, solar and climate oscillations

There are claims that there is correlation between the speed of center of mass of the solar system and the global temperature anomaly. This is partly grounded in data analysis and partly in a priori expectations. The magnitude squared coherence function is the proper measure for testing such claims. It is not hard to produce high coherence estimates at periods around 15--22 and 50--60 years between these data sets. This is done in two independent ways, by wavelets and by a periodogram method. But does a coherence of high value mean that there is coherence of high significance? In order to investigate that, four different measures for significance are studied. Due to the periodic nature of the data, only Monte Carlo simulation based on a non-parametric random phase method is appropriate. None of the high values of coherence then turn out to be significant. Coupled with a lack of a physical mechanism that can connect these phenomena, the planetary hypothesis is therefore dismissed.

Evaluation of spontaneous dense array gamma oscillatory activity and minor physical anomalies as a composite neurodevelopmental endophenotype in schizophrenia

ObjectiveMinor physical anomalies (MPAs) and gamma oscillatory activity have been proposed as associated endophenotypes in schizophrenia. Combining these endophenotypes to create a composite endophenotype may help identify those at risk for schizophrenia better. The present study aims to investigate MPAs and gamma oscillatory activity in schizophrenia patients, their unaffected first degree relatives and healthy controls and appreciate whether they can be used together as a composite endophenotype. MethodsThis was a cross sectional family study conducted at a tertiary care mental health setup. Ninety participants including schizophrenia patients, their first degree relatives and controls (thirty each) were assessed for MPAs on the Extended Waldrop Scale. All participants underwent an awake, resting 192-channel EEG recording. Spectral power and coherence in 30–100Hz gamma bands were estimated using Welch's averaged periodogram method. One-way ANOVA, chi square test were used for comparing socio-demographic-clinical variables. MANOVA supplemented by one-way ANOVAs (post hoc Tukey HSD) were done for comparison of spectral measures. Pearson's correlation, step-by-step linear discriminant functional and intra-familial correlation analysis were subsequently performed. ResultsAn endophenotype pattern of finding was found for MPAs in the craniofacial region, the total number of MPAs, spectral power in right temporal region on all bands and in the right parietal region in 50–70Hz and 70–100Hz gamma bands. The three groups were most accurately classified when MPA total score, right temporal 30–50Hz gamma power and right occipital ‘intra hemispheric’ 50–70Hz gamma coherence were considered together than when considered independently. Significant intra familial correlation was seen for MPA total score and right temporal gamma 30–50Hz power. ConclusionComposite evaluation of two developmentally linked markers i.e. MPAs and gamma spectral measures may prove useful in categorizing schizophrenia and identifying at-risk individuals.

Periodic Signal Detection in Ship Radiated Noise

Periodic signal is abundant in ship radiated noise. Its detection is an important step towards active control of ship’s acoustic signature. Based on power spectrum analysis and DEMON analysis, an approach to detect line spectrum and modulation spectrum of ship radiated noise is proposed. Periodogram method is used for power spectrum estimation. The separation of line and continuous spectrum, false positive removal, and line spectrum merging are conducted to improve the quality of detected characteristic line spectrum. Via DEMON analysis, steady physical characteristics including propeller’s rotational speed and number of blades are obtained. Finally, the effectiveness of the approach is demonstrated by simulation results.

Multipoint visualization of ULF oscillations using the Super Dual Auroral Radar Network

We present a technique for the automatic detection of Pc5 (150 s to 600 s period) ultralow frequency (ULF) pulsations in ground and ionospheric backscatter from the Super Dual Auroral Radar Network (SuperDARN). This new technique enables rapid identification and visualization of ULF oscillations over the very wide geographical coverage of SuperDARN. The technique detects ULF oscillations using the Lomb-Scargle periodogram method, which provides a natural test for periodic behavior against the null hypothesis of a pure noise distribution. This does not require any interpolation across data gaps and is thus appropriate for SuperDARN data. We demonstrate the application of the technique to SuperDARN data for March 2014 and find that Pc5 pulsations are frequently observed by multiple radars simultaneously. A preliminary investigation using data from all Northern Hemisphere SuperDARN radars indicates that Pc5 pulsation activity occurs most often on the nightside of the magnetosphere, with a mean frequency of about 2 mHz.

A comparison of dynamic cerebral autoregulation across changes in cerebral blood flow velocity for 200 s.

Objectives: The dynamic interaction between blood pressure (BP) and cerebral blood flow velocity (CBFV) is not fully understood, especially for CBFV changes lasting longer than 50 s. The interaction between BP and CBFV is relatively well characterized for periods <50 s using transfer function (TF) estimations of phase, gain, and coherence. We used TF estimations to compare the phase and gain for periods >50 s with those for periods <50 s.Materials and Methods: BP and CBFV (of the middle cerebral artery) were simultaneously recorded in 23 healthy subjects (10 men, 13 women, mean age 35 ± 10 years) under normo- and hypocapnia (induced by hyperventilation). TF and coherence estimations were based on Welch's periodogram method with a windowing of 200 s (frequency resolution, 0.005 Hz, corresponding to a period of 200 s). Means of the phase, gain, and coherence were calculated over frequency periods of 0.005–0.02 Hz (sVLF), 0.02–0.07 Hz (VLF), 0.07–0.15 Hz (LF), and 0.15–0.40 Hz (HF) and analyzed using the t-test and Pearson correlation.Results: Compared with the VLF range, normo- and hypocapnia phases were slightly but significantly lower in sVLF, while gain and coherence were not different. Hypocapnia induced significant (mostly p < 0.01) phase increases and gain decreases as well as coherence decreases in all frequency ranges. The phase and gain correlated significantly (−0.87 < r > −0.99) (p < 0.001) and inversely in all frequency ranges <0.15 Hz under both respiratory conditions. In some instances, the phase indicated disturbed autoregulation.Conclusion: In the frequency range <0.15 Hz, the phase and gain correlate highly and linearly with high consistency. The phase, gain, and coherence were similar in sVLF and VLF ranges. The phase was slightly lower in the sVLF range than in the VLF range. Notably, the data suggest that autoregulatory failure may occur in healthy persons.