Bibliography on cyclostationarity

Serpedin, Erchin; Panduru, Flaviu; Sari, Ilkay; Giannakis, Georgios B.

doi:10.1016/j.sigpro.2005.05.002

Cited by 117 publications

(67 citation statements)

References 975 publications

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“…In this specific example, the data could have been referred to as cyclostationary and analyzed by tools dedicated to such processes [20]. However, it has to be stressed that no such a priori modeling is assumed in the proposed methodology, and that the existence of a typical scale of stationarity (related to the periodic correlation) naturally emerges from the analysis.…”

Section: What Is Shown Inmentioning

confidence: 99%

Testing Stationarity With Surrogates: A Time-Frequency Approach

Borgnat

Flandrin

Honeiné

et al. 2010

IEEE Trans. Signal Process.

149

105

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Abstract-An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogates for defining the null hypothesis of stationarity and to base on them two different statistical tests. The first one makes use of suitably chosen distances between local and global spectra, whereas the second one is implemented as a one-class classifier, the time-frequency features extracted from the surrogates being interpreted as a learning set for stationarity. The principle of the method and of its two variations is presented, and some results are shown on typical models of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.

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Section: What Is Shown Inmentioning

confidence: 99%

Testing Stationarity With Surrogates: A Time-Frequency Approach

Borgnat

Flandrin

Honeiné

et al. 2010

IEEE Trans. Signal Process.

149

105

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“…The crosscorrelation matrix ( ) can also be defined by simply changing by in the expression (21). Relevant reviews of theory and applications of the cyclostationary property can be found in [50], [51] and [52].…”

Section: Cyclostationary Propertiesmentioning

confidence: 99%

Decomposition of Multi-Channel Intramuscular EMG Signals by Cyclostationary-Based Blind Source Separation

Roussel

Ravier

Haritopoulos

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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We propose a novel decomposition method for electromyographic (EMG) signals based on blind source separation. Using the cyclostationary properties of motor unit action potential trains (MUAPt), it is shown that MUAPt can be decomposed by joint diagonalization of the cyclic spatial correlation matrix of the observations. After modeling of the source signals, we provide the proof of orthogonality of the sources and of their delayed versions in a cyclostationary context. We tested the proposed method on simulated signals and showed that it can decompose up to 6 sources with a probability of correct detection and classification >95%, using only 8 recording sites. Moreover, we tested the method on experimental multi-channel signals recorded with thinfilm intramuscular electrodes, with a total of 32 recording sites. The rate of agreement of the decomposed MUAPt with those obtained by an expert using a validated tool for decomposition was >93%.Index Terms -EMG, Decomposition, Motor Unit, Firing Rate, Blind Source Separation, Cyclostationary Process, CycloSOBI, convolutive mixture, pulse train separation I IntroductionThe muscle contraction is generated by functional units --the motor units (MU, [1]) --each composed of a motoneuron (MN) innervating a group of muscle fibers. MNs generate electrical impulses, the action potentials (AP), which induce the activation of muscle fibers. The contraction force is controlled by the number of recruited MUs (recruitment) and by the density of APs within the train of each MU (rate coding) [2].The density of APs can be quantified by the inter-spike interval (ISI) or by its inverse value which is referred to as the firing rate (FR). These quantities can be defined in an instantaneous way, or with average values, or through their distributions. The EMG signal comprises the sum of the MUAP trains (MUAPt) of the active MUs. The identification of the MUAPt from the EMG provides the means to identify the neural drive to muscles. This procedure is often termed decomposition of the EMG and has provided fundamental insights into motor unit physiology and neural control of movement [3][4]. In addition to basic neurophysiologic investigations, decomposition of the EMG has also clinical relevance [5]. Acknowledgments The present work is part of the French national project ECOTECH (www.echotechsan.org) which is supported by the French National Agency for research, contract no ANR-12-TECS-0020. The work was also partly funded by the European Research Council (ERC) via the ERC Advanced Grant DEMOVE (No. 267888; to DF). The authors are grateful to Silvia Muceli for providing the experimental data.In this paper, we address the problem of EMG decomposition. Methods for intramuscular EMG decomposition date back to the 70's. In the earlier approaches, the user usually had to manually select and classify the detected MUAPs in order to identify the [34]. The BSS approach has been recently used also for decomposing multi-channel intramuscular EMG signals [35].Here, we propose a new cyclostationary based B...

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“…During last ve decades the APC class was broadly applied in telecommunication ( [17] and [18]), climatology [19] and many others elds. For exhaustive review of possible applications see [20] or [21]. Empirical importance of such a class of nonstationary time series prompted new studies concerning properties and estimation methods.…”

Section: Basic Notation and Denitionsmentioning

confidence: 99%

Almost Periodically Correlated Time Series in Business Fluctuations Analysis

Lenart

Pipień

2013

Acta Phys. Pol. A

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We propose a non-standard subsampling procedure to make formal statistical inference about the business cycle, one of the most important unobserved feature characterising uctuations of economic growth. We show that some characteristics of business cycle can be modelled in a non-parametric way by discrete spectrum of the almost periodically correlated time series. On the basis of estimated characteristics of this spectrum business cycle is extracted by ltering. As an illustration we characterise the main properties of business cycles in industrial production index for Polish economy.

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Bibliography on cyclostationarity

Cited by 117 publications

References 975 publications

Testing Stationarity With Surrogates: A Time-Frequency Approach

Testing Stationarity With Surrogates: A Time-Frequency Approach

Decomposition of Multi-Channel Intramuscular EMG Signals by Cyclostationary-Based Blind Source Separation

Almost Periodically Correlated Time Series in Business Fluctuations Analysis

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