Implementation of Non-Integer Order Filtering for the Purpose of Disparities Detection in Beta Frequencies - A Pilot Study

Kawala-Janik, Aleksandra; Zolubak, Magda; Bauer, Waldemar; Nazimek, Bartosz; Sobolewski, Tomasz; Martínek, Radek; Sowa, Marcin; Pelc, Mariusz

doi:10.1109/mmar.2018.8486113

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Medicine professionals try to use FO filters in diagnostics through analysis of electroencephalograms [42]. We find also FO implementation in bio-impedance measurements of skin tissue models [43].…”

Section: Introductionmentioning

confidence: 83%

Influence of Methods Approximating Fractional-Order Differentiation on the Output Signal Illustrated by Three Variants of Oustaloup Filter

Wiora

2020

Symmetry

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Fractional-order (FO) differential equations are more and more frequently applied to describe real-world applications or models of phenomena. Despite such models exhibiting high flexibility and good fits to experimental data, they introduce their inherent inaccuracy related to the order of approximation. This article shows that the chosen model influences the dynamic properties of signals. First, we calculated symbolically the steady-state values of an FO inertia using three variants of the Oustaloup filter approximation. Then, we showed how the models influence the Nyquist plots in the frequency domain. The unit step responses calculated using different models also have different plots. An example of FO control system evidenced different trajectories dependent on applied models. We concluded that publicized parameters of FO models should also consist of the name of the model used in calculations in order to correctly reproduce described phenomena. For this reason, the inappropriate use of FO models may lead to drawing incorrect conclusions about the described system.

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Section: Introductionmentioning

confidence: 83%

Influence of Methods Approximating Fractional-Order Differentiation on the Output Signal Illustrated by Three Variants of Oustaloup Filter

Wiora

2020

Symmetry

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“…The use of non-integer filters in the processing of biomedical signals is becoming more and more popular [3,4,8,53,[56][57][58]. This is especially in regards of analysis of EEG, EMG or ECG), as it was mentioned above [13,14,56].…”

Section: Further Research Plansmentioning

confidence: 99%

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

Kawala-Sterniuk

Podpora

Pelc

et al. 2020

Sensors

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This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

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“…Classification of biomedical data is a problem due to the nature of such signals, therefore this task is very challenging; however, classification of spectral power features of EEG data is currently quite well documented. Proper data-classification plays a crucial role in diagnostics and later in choosing the appropriate therapy (Kawala-Janik et al, 2018;Lazar et al, 2019;Martinez-Murcia et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Initial Study on Implementation of the Low-Frequency Wave Markers for the Purpose of Diagnostic Tests’ Performance and Neurofeedback Therapy

Zolubak

Pelc

Ožana

et al. 2019

Studies in Logic, Grammar and Rhetoric

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The paper focuses on automation of signal processing, which also considers analysis of biomedical data, such as EEG. The results of the study prove that this enables a better understanding of signal changes and makes it possible to address some specific disturbances. It also makes it possible to describe the relevant changes in signals mathematically and helps to create markers of various brain disorders. This paper presents the study at the initial stage and focuses on the mathematical markers of concentration disorders associated with Theta waves. The presented markers presented are based on Welch’s periodograms. The obtained results are very promising and further studies aimed at refining them are currently being carried out.

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Implementation of Non-Integer Order Filtering for the Purpose of Disparities Detection in Beta Frequencies - A Pilot Study

Cited by 7 publications

References 18 publications

Influence of Methods Approximating Fractional-Order Differentiation on the Output Signal Illustrated by Three Variants of Oustaloup Filter

Influence of Methods Approximating Fractional-Order Differentiation on the Output Signal Illustrated by Three Variants of Oustaloup Filter

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

Initial Study on Implementation of the Low-Frequency Wave Markers for the Purpose of Diagnostic Tests’ Performance and Neurofeedback Therapy

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