Quality advancement of EEG by wavelet denoising for biomedical analysis

Patil, Suhas S.; Pawar, Minal K.

doi:10.1109/iccict.2012.6398151

Cited by 19 publications

(8 citation statements)

References 9 publications

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“…measuring the electrical activity of the cerebral cortex [73][74][75][76]. EEG has been widely used since its discovery by Berger in 1929 [77]. EEG is measured using multiple electrodes that are placed on the scalp at multiple locations according to the international 10/20 placement system [78][79][80][81][82].…”

Section: Electroencephalogrammentioning

confidence: 99%

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

293

124

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Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

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Section: Electroencephalogrammentioning

confidence: 99%

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

293

124

View full text Add to dashboard Cite

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“…In reference study (S. S. Patil & Pawar, 2012), a method for denoising of the EEG signal is introduced by applying discrete wavelet transform. As a consequence of the continues nature of the CWT, it is not possible to process a real signal on a digital computer that is why another form of wavelets named DWT is used.…”

Section: Case Study Imentioning

confidence: 99%

“…Removing all the details of the original signal and moving forward with the approximation coefficients has an outcome of increasing the efficiency of denoising process. The reader is referred to 'Wavelet Transform to Advance the Quality of EEG Signals in Biomedical Analysis' for more information (S. S. Patil & Pawar, 2012)…”

Section: Case Study Imentioning

confidence: 99%

Introduction to Wavelets and their applications in signal denoising

Polat¹,

Özerdem²

2018

Bitlis Eren University Journal of Science and Technology

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The aim of this study is providing a comprehensive background information related to the roots of both Fourier Transform (FT) and Wavelet Transform (WT) along with an experiment related to applications of WT techniques. The paper describes several applications of WT and provides background information on FT. Fourier Transform (FT) is a concept that has a long history yet several issues related to resolution and uncertainty of time-frequency. Even though there are several adapted forms of FT such as Short Time Fourier Transform (STFT), which intend to solve the problems, certain limitations remain. Wavelet Transform (WT) is an alternative transformation technique emerged in order to fully tackle these diverse and complicated issues. In this paper, the background information related to the roots of FT and WT are given. Some of the problems that WT addresses are examined. WT is a tool that has many advantages among them is noise reduction and compression. We reviewed several studies that use the noise reduction capability of WT alone or combined with other signal processing tools. Discrete Wavelet Transform (DWT) based algorithm is also examined as a noise reduction technique and carried out in MATLAB setting. Analysis on a speech signal which contaminated with keyboard sound also a number spelling female voice containing unknown noise are performed. Different types of thresholding and mother wavelets were in consideration and it was revealed that Daubechies family along with the soft thresholding technique suited our application the most.

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“…The existing literature occupied with number of studies for particular application by applying various kind of wavelets which proves the importance of this technique. But, at the same time these studies fails to provide reasoning for wavelet selection [11,[15][16][17]. That's why; this study is carried out to find the best wavelet, so that maximum information could be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Such artifacts can be generated by an electrode or by the subject body itself [11,12]. The different sorts of artifacts that can happen in the signals amid recordings are the electrode noise, baseline movement, EMG disturbance, eye blink, neck movement, hand movement etc [13][14][15]. These artifacts should be expelled primarily for appropriate handling before studying the mind of the subject.…”

Section: Introductionmentioning

confidence: 99%

Optimal determination of wavelet for football player EEG using SVM classifier

Aggarwal¹,

Agarwal²

2018

biomedicalresearch

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This study investigates EEG for evaluation of the cerebrum and cognitive processes for sports' applications. An infamous issue with EEG signals for the cerebral information is frequently contaminated by artifacts of the non-cerebral cause. The nearness of curios makes the examination of EEG troublesome for sports' applications. And, to manage these artifacts, various strategies and procedures have been developed by numerous specialists applying conventional filters, artificial intelligence and time-frequency based techniques. Wavelet Transform outperforms for denoising nonstationary EEG signals, but the performance of this technique is highly dependent on wavelet selection. This paper explores an appropriate selection of particular wavelet which presents a modular statistical framework providing a pathway. A comparative analysis of 6 different wavelet families for effective filtering was done. The results show that biorthogonal wavelet family (bior3.1) was more reasonable and a classification accuracy of 91.68% was achieved for denoising the above-stated signals.

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Quality advancement of EEG by wavelet denoising for biomedical analysis

Cited by 19 publications

References 9 publications

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Introduction to Wavelets and their applications in signal denoising

Optimal determination of wavelet for football player EEG using SVM classifier

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