IF estimation for multicomponent signals using image processing techniques in the time–frequency domain

Rankine, L.; Mesbah, Mostefa; Boashash, Boualem

doi:10.1016/j.sigpro.2006.10.013

Cited by 118 publications

(84 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The proposed framework may help quantifying the inter-hemispheric functional connectivity within multi-channel newborn EEG signals. Future work will concentrate on improving the temporal resolution of the proposed approach, utilizing multi-component IF estimation techniques [41,42], and recruiting more subjects to support the statistical analysis and quantifying other newborn EEG abnormalities such as EEG asymmetry/asynchrony in preterm babies using the proposed approach. The findings of increased synchrony in seizure EEG and variable synchrony in non-seizure periods warrant exploration of the approach in a range of newborn neurological disorders where biomarkers and prognostic indicators are essential to improving management for these babies.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, IF estimation of the nonstationary signals requires a separation of the T-F components prior to estimating the IFs [10]. Although there are other methods in the literature to deal with the multicomponent nonstationary signals (e.g., see [41,42]), we adapt EMD for decomposing the signals in our proposed approach. In contrast to most existing methods, the EMD follows a fully data-driven scheme which does not need any a priori knowledge about the signal.…”

Section: Monocomponent Signals Vs Multicomponent Signals: Necessity mentioning

confidence: 99%

See 1 more Smart Citation

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

Omidvarnia

Azemi

Colditz

et al. 2013

Digital Signal Processing

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Monocomponent Signals Vs Multicomponent Signals: Necessity mentioning

confidence: 99%

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

Omidvarnia

Azemi

Colditz

et al. 2013

Digital Signal Processing

View full text Add to dashboard Cite

show abstract

“…106]. The work reported in [27] ignores the low energy regions of the TFD, but the NFM considers the entire region of the TFD.…”

Section: Nonstationary Frequency Marginalmentioning

confidence: 99%

A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity

Stevenson

O’Toole

Rankine³

et al. 2012

Medical Engineering & Physics

Self Cite

View full text Add to dashboard Cite

“…where the parameter β, (0 < β ≤ 1), controls the distribution resolution and cross-term elimination [6,30]. Generally, there is a compromise between those two TFD features, with the MBD shown to outperform many popular distributions [6,8].…”

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.

View full text Add to dashboard Cite

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].

show abstract

IF estimation for multicomponent signals using image processing techniques in the time–frequency domain

Cited by 118 publications

References 19 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

A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity

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

Contact Info

Product

Resources

About