Quantitative Comparison of Time Frequency Distribution for Heart Rate Variability Using Performance Measure

Heart Rate Variability (HRV) has been proposed as a promising non-invasive method to assess Autonomic Nervous System (ANS). The recent trend of analysing HRV, which is a non-stationary signal is using the Time Frequency (TF) analysis such as Time Frequency Distribution (TFD). However, the use of TFD is different for every application, therefore, comparison of TFD performance needs to be carried out to select the suitable TFD. The comparisons performed by previous studies were limited to visual comparison which is very subjective and could lead to error. Therefore, this paper presents an objective quantitative comparison using performance measure, M to select the suitable TFD that characterises HRV response during an Autonomic Function Test (AFT). The investigated TFDs are the Wigner Ville (WVD), Smoothed Pseudo Wigner Ville (SPWVD), Choi William (CWD), Spectrogram (SP), and recently introduced Modified B-Distribution (MBD). From the results, we conclude that MBD and SPWVD demonstrated the highest value of performance measure M, with

Time-frequency features for impedance cardiography signals during anesthesia using different distribution kernels

Objective: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. Materials and Methods: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. Results: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. Conclusion: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient’s anesthesia state in a statistically significant way.Peer ReviewedPostprint (author's final draft

Estimating the number of components of a multicomponent nonstationary signal using the short-term time-frequency Rényi entropy

This article proposes a method for estimating the local number of signals components using the short term Rényi entropy of signals in the time-frequency plane. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).The time-frequency Rényi entropy provides a measure of complexity of a nonstationary multicomponent signal in the time-frequency plane. When the complexity of a signal corresponds to the number of its components, then this information is measured as the Rényi entropy of the time-frequency distribution (TFD) of the signal. This article presents a solution to the problem of detecting the number of components that are present in short-time interval of the signal TFD, using the short-term Rényi entropy. The method is automatic and it does not require a prior information about the signal. The algorithm is applied on both synthetic and real data, using a quadratic separable kernel TFD. The results confirm that the short-term Rényi entropy can be an effective tool for estimating the local number of components present in the signal. The key aspect of selecting a suitable TFD is also discussed

An investigation into frequency resolution estimation model for impact signal analysis by using Hilbert spectrum and condition classification for marine diesel engine

In this paper, frequency resolution determination method is investigated according to Hilbert spectrum performance for impact signal analysis. A new constructed performance estimation model for the best frequency resolution is put forward in this research for the impact signal pattern recognition. Different parameters in the time-frequency distribution by using Hilbert spectrum are considered in this estimation model for the best frequency resolution determination. To verify the effectiveness of this estimation model, numerical simulation is used for Hilbert spectrum construction analysis. At the same time, different marine diesel engine working condition signals analysis are also used to illustrate the methodology developed in this research and verify the effectiveness. It can be concluded that this method can contribute the development for impact signal analysis by using Hilbert spectrum

An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR

A method for components instantaneous frequency (IF) estimation of multicomponent signals in low signal-to-noise ratio (SNR) is proposed. The method combines a new proposed modification of a blind source separation (BSS) algorithm for components separation, with the improved adaptive IF estimation procedure based on the modified sliding pairwise intersection of confidence intervals (ICI) rule. The obtained results are compared to the multicomponent signal ICI-based IF estimation method for various window types and SNRs, showing the estimation accuracy improvement in terms of the mean squared error (MSE) by up to 23%. Furthermore, the highest improvement is achieved for low SNRs values, when many of the existing methods fail.Scopu

Adaptive time-frequency distribution for accurate representation of radar signals

Electronic Support is one of the key elements in electronic warfare where the main interest is to detect and classify emitted radar signals. Quadratic time-frequency distribution (TFD) is often used to represent this type of signal due to its high resolution representation in time and frequency. However, it is greatly affected by the cross-terms which cause inaccurate signal interpretation. The purpose of this study is to design a cross-term suppression technique for a non-cooperative environment where the exact signal characteristics are unknown. A new adaptive directional ambiguity function Wigner-Ville distribution (ADAF-WVD) is developed to adaptively estimate the kernel parameters based on the ambiguity properties of a signal. Two adaptive procedures, which are the Doppler-lag block searching and the ambiguity domain energy concentration estimation are developed to separate the auto-term from the cross-term in the ambiguity domain. ADAF-WVD measures the energy level of the signal in the ambiguity domain to distinguish between the auto-terms and cross-terms. Four radar signal types are used to verify the accuracy of the time-frequency representation (TFR): simple pulse, Costas coded, pulsed linear frequency modulation and continuous wave linear frequency modulation. Accurate TFRs are produced for most of the signal as low as at signal-to-noise ratio (SNR) of -1 dB. The performance of instantaneous frequency estimation is verified using Monte Carlo simulation. Both approaches are proven to be efficient estimators as they meet the requirements of the Cramer-Rao Lower Bound at SNR > 6 dB. The computational complexity of ADAFWVD is four times lower than the adaptive smooth window cross Wigner-Ville distribution. Thus, it has been demonstrated that the developed TFD is an efficient solution for the analysis of radar signals

Linear and synchrosqueezed time–frequency representations revisited:overview, standards of use, resolution, reconstruction, concentration, and algorithms

Time–frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed versions (SWFT, SWT), provide powerful analysis tools. Here we present a thorough review of these TFRs, summarizing all practically relevant aspects of their use, reconsidering some conventions and introducing new concepts and procedures to advance their applicability and value. Furthermore, a detailed numerical and theoretical study of three specific questions is provided, relevant to the application of these methods, namely: the effects of the window/wavelet parameters on the resultant TFR; the relative performance of different approaches for estimating parameters of the components present in the signal from its TFR; and the advantages/drawbacks of synchrosqueezing. In particular, we show that the higher concentration of the synchrosqueezed transforms does not seem to imply better resolution properties, so that the SWFT and SWT do not appear to provide any significant advantages over the original WFT and WT apart from a more visually appealing pictures. The algorithms and Matlab codes used in this work, e.g. those for calculating (S)WFT and (S)WT, are freely available for download

Resolution Measure Criteria for the Objective Assessment of the Performance of Quadratic Time-Frequency Distributions

This paper presents the essential elements for developing objective methods of assessment of the performance of time-frequency signal analysis techniques. We define a measure for assessing the resolution performance of time-frequency distributions (TFDs) in separating closely spaced components in the time-frequency domain. The measure takes into account key attributes of TFDs, such as components mainlobes and sidelobes and cross-terms. The introduction of this measure allows to quantify the quality of TFDs instead of relying solely on visual inspection of their plots. The method of assessment of performance of TFDs also allows the improvement of methodologies for designing high-resolution quadratic TFDs for time-frequency analysis of multicomponent signals. Different TFDs, including the modified B distribution, are optimized using this methodology. Examples of a performance comparison of quadratic TFDs in resolving closely spaced components in the time-frequency domain, using the proposed resolution measure, are provided