Discrete wavelet transform EEG features of Alzheimer'S disease in activated states

Ghorbanian, Parham; Devilbiss, David M.; Simon, Adam J.; Bernstein, Allan L.; Hess, Terry; Ashrafiuon, Hashem

doi:10.1109/embc.2012.6346579

Cited by 18 publications

(5 citation statements)

References 25 publications

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“…Thus, more information about EEG subbands can be extracted by employing wavelet transform instead of Fourier transform which is a frequency domain approach. Many other studies have reported the use of discrete wavelet transform (DWT) in the analysis of EEG, most popularly in epilepsy [ 10 – 14 ] and Alzheimer's disease [ 15 , 16 ]. Discrete wavelet transform (DWT) has been widely used in the processing and analysis of biomedical signals as they are nonstationary.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Jacob¹,

Nair²,

Iype

et al. 2018

Neurology Research International

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EEG analysis in the field of neurology is customarily done using frequency domain methods like fast Fourier transform. A complex biomedical signal such as EEG is best analysed using a time-frequency algorithm. Wavelet decomposition based analysis is a relatively novel area in EEG analysis and for extracting its subbands. This work aims at exploring the use of discrete wavelet transform for extracting EEG subbands in encephalopathy. The subband energies were then calculated and given as feature sets to SVM classifier for identifying cases of encephalopathy from normal healthy subjects. Out of various combinations of subband energies, energy of delta subband yielded highest performance parameters for SVM classifier with an accuracy of 90.4% in identifying encephalopathy cases.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Jacob¹,

Nair²,

Iype

et al. 2018

Neurology Research International

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“…8 Hz. Therefore, we utilized filters that eliminate unnecessary frequencies and concentrate solely on the range corresponding to the five EEG rhythms that are medically recognized, specifically delta (0-4 Hz), theta (4-8 Hz), alpha (8-16 Hz), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32), and gamma (32-64 Hz). The wavelet transform is good tool used for analysis the EEG signal [14].…”

Section: B Denoising Of Eeg Signal Based On Wavelet Transformsmentioning

confidence: 99%

“…(3) Choosing the proper number of wavelet decomposition levels (or scale levels) is the first stage in the DWT decomposition. For the initial level, = 1 signal [ ] passes through both the high and low pass filter, ℎ[ ] and [ ] respectively, then the procedure of down-sampling by two [16], as illustrated in Figure 5 and denoising by using DWT is shown in Figure 6.…”

Section: ( ) = | |mentioning

confidence: 99%

Wavelet Transforms for Eeg Signal Denoising and Decomposition

Elshekhidris,

MohamedAmien,

Fragoon

2023

IJASIS

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EEG signal analysis is difficult because there are so many unwanted impulses from non-cerebral sources. Presently, methods for eliminating noise through selective frequency filtering are afflicted with a notable deprivation of EEG information. Therefore, even if the noise is decreased, the signal's uniqueness should be preserved, and decomposition of the signal should be more accurate for feature extraction in order to facilitate the classification of diseases. This step makes the diagnosis faster. In this study, three types of wavelet transforms were applied: Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), and Stationary Wavelet Transform (SWT), with three mother functions: Haar, Symlet2, and Coiflet2. Three parameters were used to evaluate the performance: Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Most of the higher values of SNR and PSNR were 27.3189 and 40.019, respectively, and the lowest value of MSE was 5.0853 when using Symlet2-SWT level four. To decompose the signal, we relied on the best filter used in the denoising process and applied four methods: DWT, Maximal Overlap DWTs (MODWT), Empirical Mode Decomposition (EMD), and Variational Mode Decomposition (VMD). The comparison has been made between the four methods based on three metrics: energy, correlation coefficient, and distances between the Power Spectral Density (PSD), where the highest value of energy was 5.09E+08 and the lowest value of the PSD was -1.2596 when using EMD.

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“…a is the scale parameter, b is the location parameter, is the wavelet function also known as the 'mother wavelet'. The superscript '*' denotes the complex conjugate of the function, and √ is used to normalize the energy such that it stays as the same level for different values of a and b (Ghorbanian, Devilbiss, Simon, Bernstein, Hess, & Ashrafiuon, 2012). Wavelet analysis overcomes the limitations of the fast Fourier Transform (FFT) by breaking up of the EEG signal into shifted and scaled versions of the original (or mother) wavelet.…”

Section: Wavelet Analysismentioning

confidence: 99%

Neural Correspondence to Human Cognition from Analysis to Intuition – Implications of Display Design for Cognition

Nuamah

Seong

2017

Proceedings of the Human Factors and Ergonomics Society Annual

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Psychophysiological measures can be used to determine whether a particular display produces a general difference in brain function. Such information might be valuable in efforts to improve usability in display design. In this preliminary study, we aimed to use the electroencephalography (EEG) task load index (TLI), given by the ratio of mean frontal midline theta energy to mean parietal alpha energy, to provide insight into the mental effort required by participants performing intuition-inducing and analysis-inducing tasks. We employed behavioral measures (reaction time and percent correct), and a subjective measure (NASA-Task Load Index) to validate the objective measure (TLI). The results we obtained were consistent with our hypothesis that mental effort required for analysis-inducing tasks would be different from that required for intuition-inducing tasks. Although our sample size was small, we were able to obtain a significant positive correlation between NASA-Task Load Index and TLI.

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Discrete wavelet transform EEG features of Alzheimer'S disease in activated states

Cited by 18 publications

References 25 publications

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Wavelet Transforms for Eeg Signal Denoising and Decomposition

Neural Correspondence to Human Cognition from Analysis to Intuition – Implications of Display Design for Cognition

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