Parametric and Non Parametric Time-Frequency Analysis of Biomedical Signals

Elouaham, Samir; Latif, Rachid; Dliou, Azzedine; Laaboubi, Mostafa; Maoulainie, F. M. R.

doi:10.14569/ijacsa.2013.040110

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…The time-frequency methods are used for revealing the frequency components over time. Among these techniques; the Periodogram (PE), the Choi-Williams (CW) and the Smoothed Pseudo Wigner-Ville (SPWV) were chosen [15,16,17,18,19].…”

Section: Time-frequency Methodsmentioning

confidence: 99%

“…In the frequency domain; the analysis of the biomedical signals is not very delicate because of the disappearance of the concept of time during the appearance of the frequency contents. The time-frequency methods may be a good tool for surpassing the cited problem of the traditional methods [15,16,17,18,19]. The time-frequency methods permit extracting different characteristics of biomedical signals as EEG and have a good resolution and high anti -noise property.…”

Section: Introductionmentioning

confidence: 99%

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Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

2021

IJATCSE

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The electroencephalogram (EEG) is a test that determines brain activity. The existence of artifacts in EEG can naturally decrease the smoothness of the analysis of the biomedical signal. EEG disturbed by noises during encephalogram recordings is one of the problems that the experts have to investigate for finding solutions to remove these artifacts. To obtain an accurate EEG signal; the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used and compared with and its old versions. The non-stationarity and non-linearity of the EEG signal cannot provide complete information when using the traditional method (Temporal and frequency domains). Among the objectives is the comparative analysis of time-frequency distributions applied on EEG signals. The denoising and time-frequency methods used in this study are tested on healthy and abnormal EEG signals which are disturbed by natural and artificial noises. The comparative study in this work shows the effectiveness of the combination of the ICEEMDAN and Periodogram methods that are suitable for denoising and analyzing the EEG signal.

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Section: Time-frequency Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

2021

IJATCSE

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“…The RLS which has more complex structure, exhibits better performance and fewer iterations. LMS and RLS are sufficient for TVAR parameter estimation if the non-stationary part of the signal changes slowly and not suitable for rapidly changing dynamics [13]. Although these methods work reasonably well for slow varying signals but they are also sensitive to noise.…”

Section: A Adaptive Methods (Am)mentioning

confidence: 99%

“…[14] To further enhance the stability, these parameters are defined within a range determined by largest eigenvalue; which is an assumed value. [13]…”

Section: A Adaptive Methods (Am)mentioning

confidence: 99%

Review of Parameter Estimation Techniques for Time-Varying Autoregressive Models of Biomedical Signals

Najeeb¹,

Salami²,

Gunawan³

et al. 2016

IJSPS

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Biomedical signals are non-stationary and a research topic of practical interest as the signal has time varying statistics. The problem of time varying is usually circumvented by assuming local stationary over a short time interval, where stationary techniques are applied. However, features extracted from these methods are not always suitable and methods for non-stationary process are needed. Time varying signals are more accurately represented by time frequency methods and received most attention recently. Among the time frequency methods, parametric modeling such as TVAR has been promising over nonparametric methods with improved resolutions and able to trace strong non-stationary signal. Despite the success of TVAR in various applications it has drawbacks. This paper presents an extensive review on TVAR modelling techniques. Different approaches for TVAR modeling is presented and outlined. Principles, advantages, disadvantages of those techniques are presented concisely. And finally a new direction has been suggested briefly.

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“…Among the existing bilinear distributions, the Choi-Williams Distribution (CWD) was proposed in different studies. [35][36][37][38] Some works have demonstrated that the Choi-Williams transformation achieved better resolution in time and frequency than the spectrogram and the scalogram. 34 40 41 Within the field of genomic signal processing, the Choi-Williams distribution was used to discriminate coding regions from non-coding regions.…”

Section: Introductionmentioning

confidence: 99%

2D DNA Representations Generated Using a New Coding and the Time-Frequency Analysis

Messaoudi¹,

Oueslati²,

Lachiri³

2015

J Med Imaging Hlth Inform

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The state of the art shows that Fourier transform based methods lack robustness in terms of the DNA characterization and detection. In fact, the Fourier analysis is limited by the shape and the fixed size of the analysis window; which can affect the representation resolution. In this paper, we review the power of the time-frequency analysis in characterizing the biological sequences. In particular, we study the performance of the wavelet analysis in distinguishing specific structures within the C. elegans genome. The method offers the possibility to visualize the genomic data within the form of explicit images in which DNA elements possess particular behavior. And thus, we can clearly identify homologous sequences through their common behavior. A comparison with the smoothed Fourier analysis and the Choi-Williams distribution is then established. Even the Smoothed Fourier technique is limited as it is a fixed resolution method; thereby the multi-resolution analysis is proposed to improve the DNA analysis. On the other hand, the Choi-Williams distribution increases the time-frequency resolution. Nevertheless, it exhibits the presence of cross-terms which can be considered as signal components. Most of all, one must digitize the DNA characters to be able to use the proposed tools. In line with this, we use the Frequency Chaos Game Signal (FCGS) as coding technique. The FCGS is built in such way that we can follow the frequency evolution of nucleotides' occurrence along the genome. Through this work, we demonstrate that color-scalogram is more effective than the spectrogram and the Choi-Williams representations; since it enables the characterization of different DNA types by specific texture. Besides the visual inspection of the representations, the Shannon entropy measure proves the effectiveness of the wavelet method.

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Parametric and Non Parametric Time-Frequency Analysis of Biomedical Signals

Cited by 6 publications

References 14 publications

Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

Review of Parameter Estimation Techniques for Time-Varying Autoregressive Models of Biomedical Signals

2D DNA Representations Generated Using a New Coding and the Time-Frequency Analysis

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