Resolution measure criteria for the objective assessment of the performance of quadratic time-frequency distributions

Boashash, Boualem; Sucic, Victor

doi:10.1109/tsp.2003.810300

Cited by 89 publications

(68 citation statements)

References 12 publications

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“…The resulting [2]. Our selection is also based on the comparison of TFDs in [24] where SPEC, MBD and CWD are ranked within the first four high-resolution TFDs. Table 1 summarizes the discrete forms of the time-lag kernels and their parameters utilized in this study.…”

Section: Quadratic Tfdsmentioning

confidence: 99%

“…Since the test evaluates more than one cointegrating relationship within the time series, it is applicable for multivariate signals such as multi-channel EEG. The starting point of the test in this study is from an N-dimensional IF f z [n] ∈ N×1 as a multivariate MVAR model of order p given by [11,55]: (24) where A r ∈ N×N is the MVAR coefficient matrix at delay r, ε[n] ∈ N×1 is the input white noise of the model and μ ∈ N×1 is a constant term. The assumption here is that the variables of f z [n] are integrated processes of either order one (I(1)) or order zero (I(0) or a stationary process).…”

Section: Appendix a Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

“…The assumption here is that the variables of f z [n] are integrated processes of either order one (I(1)) or order zero (I(0) or a stationary process). The MVAR model described in (24) can be re-written in terms of the differences between its successive delayed values as: (25) where…”

Section: Appendix a Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

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A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

Omidvarnia

Azemi

Colditz

et al. 2013

Digital Signal Processing

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This paper proposes a new approach to estimate the phase synchrony among nonstationary multivariate signals using the linear relationships between their instantaneous frequency (IF) laws. For cases where nonstationary signals are multi-component, a decomposition method like multi-channel empirical mode decomposition (MEMD) is used to simultaneously decompose the multi-channel signals into their intrinsic mode functions (IMFs). We then apply the Johansen method on the IF laws to assess the phase synchrony within multivariate nonstationary signals. The proposed approach is validated first using multichannel synthetic signals. The method is then used for quantifying the inter-hemispheric EEG asynchrony during ictal and inter-ictal periods using a newborn EEG seizure/non-seizure database of five subjects. For this application, pair-wise phase synchrony measures may not be able to account for phase interactions between multiple channels. Furthermore, the classical definition of phase synchrony, which is based on the rational relationships between phases, may not reveal the hidden phase interdependencies caused by irrational long-run relationships. We evaluate the performance of the proposed method using the differentiation of unwrapped phase as well as other IF estimation techniques. The results obtained on newborn EEG signals confirm that the generalized phase synchrony within EEG channels increases significantly during ictal periods. A statistically consistent phase coupling is also observed within the non-seizure segments supporting the concept of constant inter-hemispheric connectivity in the newborn brain during inter-ictal periods.

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Section: Quadratic Tfdsmentioning

confidence: 99%

Section: Appendix a Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

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A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

Omidvarnia

Azemi

Colditz

et al. 2013

Digital Signal Processing

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“…Various reduced interference distributions (RIDs) have been proposed in order to have a high resolution timefrequency signal representation, such as modified B distribution (MBD) [6] and the RID based on the Bessel kernel [7], both used in this paper. A measure for time-frequency resolution and component separation was proposed in [8].…”

Section: Eurasip Journal On Advances In Signal Processingmentioning

confidence: 99%

“…Generally, there is a compromise between those two TFD features, with the MBD shown to outperform many popular distributions [6,8]. Furthermore, the MBD was also proven to be a suitable TFD for robust IF estimation [6].…”

Section: Tfd Selectionmentioning

confidence: 99%

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

Lerga

Sucic

Boashash

2011

EURASIP J. Adv. Signal Process.

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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. Signal Model and Problem FormulationMany signals in practice, such as those found in speech processing, biomedical applications, seismology, machine condition monitoring, radar, sonar, telecommunication, and many other applications are nonstationary [1]. Those signals can be categorized as either monocomponent or multicomponent signals, where the monocomponent signal, unlike the multicomponent one, is characterized in the timefrequency domain by a single "ridge" corresponding to an elongated region of energy concentration [1].For a real signal s(t), its analytic equivalent z(t) is defined aswhere H {s(t)} is the Hilbert transformation of s(t), a(t) is the signal instantaneous amplitude, and φ(t) is the signal instantaneous phase. The instantaneous frequency (IF) describes the variations of the signal frequency contents with time; in the case of a frequency-modulated (FM) signal, the IF represents the FM modulation law and is often referred to as simply the IF law [2,3]. The IF of the monocomponent signal z(t) is the first derivative of its instantaneous phase, that is, ω(t) = φ (t) [1]. Furthermore, the crest of the "ridge" is often used to estimate the IF of the signal z(t) as [1]whereOn the other hand, the analytical multicomponent signal x(t) can be modeled as a sum of two or more monocomponent signals (each with its own IF ω m (t))where M is the number of signal components, a m (t) is the mth component instantaneous amplitude, and φ m (t) is its instantaneous phase. When calculating the Hilbert transform of the signal s(t) in (1), the conditions of Bedrosian's theorem need to be satisfied, that is, a(t) has to be a low frequency function with the spectrum which does not overlap with the e jφ(t) spectrum [2-5].

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Motion-Based Gait Identification Using Spectro-temporal Transform and Convolutional Neural Networks

et al. 2018

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Resolution measure criteria for the objective assessment of the performance of quadratic time-frequency distributions

Cited by 89 publications

References 12 publications

A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

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

Motion-Based Gait Identification Using Spectro-temporal Transform and Convolutional Neural Networks

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