EEG analysis based on wavelet-spectral entropy for epileptic seizures detection

Mirzaei, Ahmad; Ayatollahi, Ahmad; Gifani, Parisa; Salehi, Leili

doi:10.1109/bmei.2010.5639894

Cited by 31 publications

(25 citation statements)

References 12 publications

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“…The abovementioned equations help us in determining the peaks and valleys in the original recording [7]. The opening function (erosion-dilation) is used in smoothing of the convex peak of the original signal, and the closing function (dilation-erosion) is used in smoothing the concave peak of the signal.…”

Section: Morphological Filtering For Feature Extraction Of Eeg Signalsmentioning

confidence: 99%

“…With real-time monitoring to detect epileptic seizures gaining widespread recognition, the advent of computers has made it possible to effectively apply a host of methods to quantify the changes occurring based on the EEG signals [4]. The EEG is an important clinical tool for diagnosing, monitoring, and managing neurological disorders related to epilepsy [7]. This disorder is characterized by sudden recurrent and transient disturbances of mental function and/or movements of body that results in excessive discharge group of brain cells [8].…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

Rajaguru¹,

Vijayakumar²

2014

EURASIP J. Adv. Signal Process.

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The objective of this paper is to compare the performance of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from electroencephalogram (EEG) signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals. Out of which four parameters like positive and negative peaksand spike and sharp waves, events and average duration are extracted using Haar, dB2, dB4, and Sym 8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. Then, the performance of the code converter and classifiers are compared based on the parameters such as performance index (PI) and quality value (QV).The performance index and quality value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.

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Section: Morphological Filtering For Feature Extraction Of Eeg Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

Rajaguru¹,

Vijayakumar²

2014

EURASIP J. Adv. Signal Process.

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“…2 Electroencephalography (EEG) signals are enumerated as the representation of postsynaptic potentials that are generated at a cortical level by synchronous activity of about 10 5 neurons. 3 EEG is a field of study that is prominently deployed to diagnose various conscious and subconscious brain activities. 4 Nevertheless, the detection of seizures from a visual inspection of the EEG scan can be challenging even for a trained neurologist for a variety of reasons, such as excessive presence of myogenic artifacts, 5 power frequency coupling, etc.…”

Section: Introductionmentioning

confidence: 99%

A Stand-Alone EEG Monitoring System for Remote Diagnosis

Thangavelu¹,

Kasthuri²,

Sundaram

et al. 2016

Telemedicine and e-Health

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“…Moreover, biomedical signals like EEG differ from person to person and also from time to time in the same record. Hence, computer analysis is necessary and highly useful [1]. Earlier, Fourier transform and Fast Fourier transform were used for feature extraction but the limitation of these methods is that they are highly sensitive to noise [1].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, computer analysis is necessary and highly useful [1]. Earlier, Fourier transform and Fast Fourier transform were used for feature extraction but the limitation of these methods is that they are highly sensitive to noise [1]. Furthermore, the nature of EEG signals is non-stationary and to analyse them, we need time-frequency analysis technique.…”

Section: Introductionmentioning

confidence: 99%

Epileptic seizure detection using PCA on wavelet subbands

Sheoran

Saini

2014

2014 5th International Conference - Confluence the Next Generation Information Technology Summit (Confluence)

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The detection and classification of epileptic seizures using the Electroencephalography (EEG) signal has been an active field of research from past few decades. EEG is a nonstationary signal that represents electrical activity along the scalp containing very useful information about normal or epileptic brain states. In this paper, principal component analysis is performed on the wavelets subbands of normal & epileptic signals using discrete wavelet transform. This method is applied to two different groups of EEG signals, i.e., (1) Healthy states (2) Epileptic states, during a seizure (ictal EEG). The features extracted from the principal components that are evaluated from the wavelet subbands, differentiate between these two states. Further, t-student statistical distribution is applied to determine the measure of distinguishing between different subjects. The method of principal component analysis on wavelet subbands can discriminate between ictal & non-ictal subjects with 99.99% pvalue (eye open) and 99.96% p-value (eye closed) using the delta subband. The results presented here are much better than the results of previous researches.

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EEG analysis based on wavelet-spectral entropy for epileptic seizures detection

Cited by 31 publications

References 12 publications

Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

A Stand-Alone EEG Monitoring System for Remote Diagnosis

Epileptic seizure detection using PCA on wavelet subbands

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