Time–frequency feature representation using energy concentration: An overview of recent advances

Sejdić, Ervin; Djurović, Igor; Jiang, Jin

doi:10.1016/j.dsp.2007.12.004

Cited by 675 publications

(332 citation statements)

References 225 publications

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“…The structure of this algorithm was built using typical approaches to signal processing, which can be seen in Fig. 19, originated from (Sejdic et al, 2009).…”

Section: Numerical Evaluation Of the Proposed Classification Methodsmentioning

confidence: 99%

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Swiercz¹

2010

International Journal of Applied Mathematics and Computer Science

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A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requires a priori knowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to the max discriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

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“…The structure of this algorithm was built using typical approaches to signal processing, which can be seen in Fig. 19, originated from (Sejdic et al, 2009).…”

Section: Numerical Evaluation Of the Proposed Classification Methodsmentioning

confidence: 99%

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Swiercz¹

2010

International Journal of Applied Mathematics and Computer Science

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“…TFR should be used when there is evidence of time-varying or non-stationary conditions on the signal. In such cases, the time or the frequency domain descriptions of the signal alone cannot provide comprehensive information for analysis and classification, thus t-f methods should outperform conventional analysis methods (Sejdic et al, 2009;Tzallas et al, 2008). In the past, different forms of estimating TFR have been proposed.…”

Section: Time-frequency Representations and Time-frequency Dynamic Fementioning

confidence: 99%

“…Classification based on local regions of the t-f plane has achieved higher success rates than those based on the entire t-f plane (Tzallas et al, 2008), but there is a significant unsolved issue associated with local-based analysis, which is the selection of the size and location of relevant regions. As a result, the choice of the feature extractor in the t-f domain is highly dependent on the final application (Sejdic et al, 2009). Relevance analysis is a tool that may serve to select the most informative t-f features from a discriminative viewpoint.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Based Feature Extraction for Non-Stationary Signal Classification

Avendaño-Valencia¹,

Acosta-Medina²,

Castellanos-Domínguez³

2011

Applied Biomedical Engineering

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“…In the photoplethysmography, a correlation of changes in the pulse width of the measured photoplethysmographic signal with changes in peripheral vascular resistance was found in [3]. The paper [4] provides an overview of methods dealing with energy concentration in the time-frequency domain. The results of the literature review indicate that using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications.…”

Section: Introductionmentioning

confidence: 99%

Requirements on Needed Frequency Bandwidth Depending on Pulse Waveforms and Their Allowed Distortion

Sigmund

Brančík

2016

Journal of Electrical Engineering

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Time–frequency feature representation using energy concentration: An overview of recent advances

Cited by 675 publications

References 225 publications

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Time-Frequency Based Feature Extraction for Non-Stationary Signal Classification

Requirements on Needed Frequency Bandwidth Depending on Pulse Waveforms and Their Allowed Distortion

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