Instantaneous frequency estimation from FM signals and its use in continuous phase modulation receivers

Abeysekera, S.S.

doi:10.1109/icics.2009.5397712

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…The cointegration ranks of the IMFs are then utilized for calculating the GePS measure of the underlying segment using (19). A vertical frequency shift can be observed between each two successive rows in Fig.…”

Section: Significant Increment Of the Geps Measure During The Seizurementioning

confidence: 99%

“…Dealing with nonstationary signals whose frequency content changes over time is a common situation in many engineering areas including biomedical signal processing [6,14,15], radar [16][17][18] and telecommunications [19,20]. For the analysis of nonstationary signals, T-F distributions (TFDs) are the most suitable tools as they provide two-dimensional representations that reflect the time-varying spectral characteristics of the nonstationary signal and show how the energy of the signal is distributed over the twodimensional T-F space.…”

Section: Quadratic Tfdsmentioning

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: Significant Increment Of the Geps Measure During The Seizurementioning

confidence: 99%

Section: Quadratic Tfdsmentioning

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

“…Dealing with non-stationary signals whose frequency content changes over time is a common situation in many engineering areas including biomedical signal processing [53,61,62], radar [63][64][65] and telecommunications [66,67]. [3].…”

Section: Quadratic Tfdsmentioning

confidence: 99%

Newborn EEG connectivity analysis using time-frequency signal processing techniques

Omidvarnia¹

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Scalp electroencephalography (EEG) monitoring in early life is providing vital assistance to clinical management during diagnosis and treatment of developing brain disorders. It is also a useful tool for predicting long-term neurological outcomes of perinatal injuries. Continuous evolution of cerebral structures during early brain maturation imposes salient changes in the temporal morphology of newborn EEG patterns. Therefore, dealing with neonatal EEG signals is technically very challenging. The current dominant approach to newborn EEG assessment is based on visual inspection of EEG recordings by an expert. This process is highly subjective and also, very timeconsuming. More importantly, it requires a high level of expertise for appropriate interpretation. On the other hand, neonatal intensive care units (NICUs) in most countries do not have ready access to high-level EEG expertise. The development of automatic newborn EEG analysis systems is therefore vital to improve international newborn health.This dissertation is focused on developing scalp EEG connectivity analysis methods for objective monitoring of newborn EEG signals. Three important characteristics of the signals are taken into consideration for the proposed methodologies: time-varying behavior, directional relationships between channels and frequency-specific fluctuations of amplitudes. It leads to a more comprehensive insight into the scalp-level electrical interactions between different cortical areas of the newborn brain in time and/or frequency domains. Two types of newborn brain abnormalities including seizures and intra-ventricular hemorrhage (IVH) and their impact on scalp EEG connectivity are investigated. Also, neonatal electric resting state networks (eRSNs) are characterized using EEG recordings of healthy fullterms, healthy preterms and preemies with IVH as well as a set of newborn functional magnetic resonance imaging (fMRI) datasets.The first contribution of this research is a new time-frequency based approach for estimating the generalized phase synchrony (GePS) among multichannel newborn EEG signals using the linear relationships between their instantaneous frequency laws. Since the underlying signals are usually multicomponent, a decomposition method like multi-channel empirical mode decomposition is used to simultaneously decompose the multi-channel signals into their intrinsic mode functions (IMFs). The results confirm that the GePS value 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 interictal periods.The second contribution is a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded newborn EEG signals in a way that is less affected by volume conduction and amplitude scaling. The time-varying generalized partial directed coherence method is modified, by orthogonalization ...

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Instantaneous frequency estimation from FM signals and its use in continuous phase modulation receivers

Cited by 2 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

Newborn EEG connectivity analysis using time-frequency signal processing techniques

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