Analysis of local time-frequency entropy features for nonstationary signal components time supports detection

Sucic, Victor; Saulig, Nicoletta; Boashash, Boualem

doi:10.1016/j.dsp.2014.07.013

Cited by 37 publications

(26 citation statements)

References 12 publications

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“…As major limitation, constant amplitude as well as non-overlapping components are also required to give a correct final result. In particular, the experimental results presented in [34,35] have confirmed method limitation in overlapped modes analysis. Precisely, in the cases shown in Figure 1, Rényi entropy-based method is expected to detect only one component at the interference regions.…”

Section: Introductionsupporting

confidence: 52%

“…In order to compare the proposed method to the ones in the literature, Figure 14 shows the estimated number of modes of the signal in Figure 11a by using the Rényi entropy-based approach [34,35]. The required parameters have been set according to [35]. As it can be observed, the result in Figure 14b is affected by the presence of boundary effects in spectrogram domain (around u = 350).…”

Section: Resultsmentioning

confidence: 99%

“…It turns out that this approach remains limited to linearly FM signals. A method for estimating the number of modes of an FM signal has been proposed in [34,35]. This paper introduced a local version of Rényi entropy, namely Short-term Rényi entropy, as a measure of signal complexity, i.e., the number of components.…”

Section: Introductionmentioning

confidence: 99%

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A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

2020

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Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.

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Section: Introductionsupporting

confidence: 52%

Section: Resultsmentioning

confidence: 99%

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A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

2020

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“…It is worth observing that the assumption on the number of components can be relaxed by adopting a counting procedure, for instance, the one proposed in [36], which is based on the analysis of the short-term Rényi entropy and allows for estimating the local number of components, even in the overlapping case.…”

Section: Extraction Of Rsd Informative Contentmentioning

confidence: 99%

Radon spectrogram-based approach for automatic IFs separation

Bruni

Tartaglione

Vitulano

2020

EURASIP J. Adv. Signal Process.

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The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In order to estimate the instantaneous frequencies of a multicomponent signal, it is necessary to disentangle signal modes in a proper domain. Unfortunately, if signal modes supports overlap both in time and frequency, separation is only possible through a parametric approach whenever the signal class is a priori fixed. In this work, time-frequency analysis and Radon transform are jointly used for the unsupervised separation of modes of a generic frequency modulated signal in noisy environment. The proposed method takes advantage of the ability of the Radon transform of a proper time-frequency distribution in separating overlapping modes. It consists of a blind segmentation of signal components in Radon domain by means of a near-to-optimal threshold operation. The inversion of the Radon transform on each detected region allows us to isolate the instantaneous frequency curves of each single mode in the time-frequency domain. Experimental results performed on constant amplitudes chirp signals confirm the effectiveness of the proposed method, opening the way for its extension to more complex frequency modulated signals.

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“…The main difference among time-frequency methods is the way they handle the problem of uncertainty. Timefrequency methods have been used in various works for nonstationary signal analysis [24,25] and for seizure detection [26,27].…”

Section: Feature Extractionmentioning

confidence: 99%

A Multistage System for Automatic Detection of Epileptic Spikes

Anh-Dao¹,

Linh-Trung

Nguyen

et al. 2018

REV J. Electron. Commun.

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Abstract-A multistage automatic detection system for epileptic spikes is introduced as an assistant tool for epileptic analysis and diagnosis based on electroencephalogram (EEG). The system consists of four stages: preprocessing, feature extraction, classifier and expert system. Multiple state-of-the-art signal processing and machine learning techniques including wavelet transform, spectral filtering, artificial neural network are utilized in order to improve the ability of the overall system, stage by stage. Compared to other works, our contributions are three-fold: peaks in the EEG recording are categorized into two groups of non-epileptic spikes and possible epileptic spikes by a committee of three perceptrons; appropriate mother wavelet and wavelet scales are selected for the best system performance; and, based on the neurological fact that an epileptic spike is usually followed by a slow wave, a simple expert system is presented to eliminate pseudo-spikes which are closely analogous to true epileptic spikes. Experimental results show that the proposed system is capable of detecting epileptic spikes efficiently.

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Analysis of local time-frequency entropy features for nonstationary signal components time supports detection

Cited by 37 publications

References 12 publications

A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

Radon spectrogram-based approach for automatic IFs separation

A Multistage System for Automatic Detection of Epileptic Spikes

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