EEG signal enhancement using cascaded S-Golay filter

Agarwal, Seema; Rani, Asha; Singh, Vijander; Mittal, Alok Prakash

doi:10.1016/j.bspc.2017.04.004

Cited by 42 publications

(15 citation statements)

References 45 publications

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“…Based on literature review and authors' experience, it is important to choose appropriate filtering methods, so that important information would not be lost. Some work, where a similar study was performed, suggest choosing (while filtering using Savitzky-Golay filters) as high as possible window length N and as low as possible frame L [24]. However, the tests carried out by the authors of this paper proved that higher N value smoothed the signals too strongly and the obtained results were less legible and less useful for the diagnostics' purposes.…”

Section: Methodsmentioning

confidence: 90%

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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Section: Methodsmentioning

confidence: 90%

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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“…To verify the influence of K value selection, an artificial EEG signal was used. It was generated according to the mathematical description given in [1]. The obtained composite waveform contains typical variabilities of the EEG signal [1].…”

Section: Selection Of Number Of Overlapping Samplesmentioning

confidence: 99%

“…To test the proposed COWA filter, 40 signals from 5 young subjects (age ranging from 1.5 to 22 years) from the CHB-MIT database were used. This database consists of EEG recordings from paediatric subjects with intractable seizures [1,14]. To evaluate the quality of filtering, the SNR improvement factor was introduced…”

Section: The Real Eeg Signalsmentioning

confidence: 99%

“…The sharpness of the EEG signal must be retained efficiently during the preprocessing stage, but any artefacts or outliers that appear in the signal should be effectively suppressed [1]. EEG signals are contaminated with various artefacts, such as signals associated with muscle activity, eye movements and blinks, body motions or power grid noise, which are not of cerebral origin [1,2]. These types of disturbances appear as high-amplitude or high-frequency bursts of activity and look like impulsive types of noise, whereas brain activities have generally lower energy than artefacts.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, advanced machine learning methods have been developed to deal with the artefacts. They include such methods as independent component analysis (ICA), canonical correlation analysis (CCA) [3], discrete wavelet transform (DWT), empirical mode decomposition (EMD) or cascaded Savitzky-Golay filter [1,2,4]. Neurologists have been working on the interpretation of EEG signals, but there are a few cases when real-time monitoring or automatic interpretation is particularly important.…”

Section: Introductionmentioning

confidence: 99%

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EEG signal improvement with cascaded filter based on OWA operator

Pander

2019

SIViP

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Noise reduction methods have a great impact on the performance of all EEG signal processing systems. The work involves the reduction in impulsive interferences. It is impossible to suppress effectively such a noise using linear filtering approach. The work presents the properties of a cascaded filter based on the ordered weighted aggregation (OWA) operator and its application to improve the electroencephalogram (EEG) signal. The OWA operator is a class of mean-like aggregation operators. By introducing a nonlinear sorting process and by assigning appropriate values of weights to the sorted samples, the improvement in filtering in the impulsive environment is achieved. The structure of the proposed cascade consists of two layers. The first layer contains two OWA filters that share a certain number of signal samples. The second layer averages the outputs of the first layer. The algorithm for the rise of filtering efficiency has been introduced. The performance of the new method has been experimentally compared with the traditional methods of impulsive noise reduction, using synthetic as well as real signals from the CHB-MIT database. The obtained results demonstrate that in the field of impulsive noise suppression and preprocessing, the proposed cascaded OWA filter brings about a significant improvement in the signal-to-noise ratio.

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Machine Learning and Electronic Noses for Medical Diagnostics

Wojnowski¹,

Kalinowska²

2021

Artificial Intelligence in Medicine

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EEG signal enhancement using cascaded S-Golay filter

Cited by 42 publications

References 45 publications

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

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

EEG signal improvement with cascaded filter based on OWA operator

Machine Learning and Electronic Noses for Medical Diagnostics

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