Linear Prediction Modelling for the Analysis of the Epileptic EEG

Yarlagadda, Prasad K.D.V.; Subbarao, K.T.V; Malini, V.; Rao, C. Raghavendra

doi:10.1109/ace.2010.20

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…Khan et al [9] have applied wavelet transform for preprocessing. Other methods of noise removal from EEG signals include surrogate channel [56] with the help of common spatial pattern filtering [57], local mean decomposition [54] and adaptive filtering [58], [59].…”

Section: A Pre-processingmentioning

confidence: 99%

Epileptic Seizures Prediction Using Deep Learning Techniques

2020

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Epilepsy is a very common neurological disease that has affected more than 65 million people worldwide. In more than 30 % of the cases, people affected by this disease cannot be cured with medicines or surgery. However, predicting a seizure before it actually occurs can help in its prevention; through therapeutic intervention. Studies have observed that abnormal activity inside the brain begins a few minutes before the start of a seizure, which is known as preictal state. Many researchers have tried to find a way for predicting this preictal state of a seizure but an effective prediction in terms of high sensitivity and specificity still remains a challenge. The current study, proposes a seizure prediction system that employs deep learning methods. This method includes preprocessing of scalp EEG signals, automated features extraction; using convolution neural network and classification with the support of vector machines. The proposed method has been applied on 24 subjects of scalp EEG dataset of CHBMIT resulting in successfully achieving an average sensitivity and specificity of 92.7% and 90.8% respectively.

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Section: A Pre-processingmentioning

confidence: 99%

Epileptic Seizures Prediction Using Deep Learning Techniques

2020

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“…15 Some researchers were keen about the selection of the database as well. Numerous studies have been conducted for the prediction and detection of seizures, such as using linear prediction, 18 using cloud computing technology, 19 using deep learning methods, 20 using wavelet packets, 21 etc. Majority of the previous studies as in other works 3,4,17 were analyzed and found that due to the uncorrelated data used, the results are not consistent.…”

Section: Related Workmentioning

confidence: 99%

“…Majority of the previous studies as in other works 3,4,17 were analyzed and found that due to the uncorrelated data used, the results are not consistent. Numerous studies have been conducted for the prediction and detection of seizures, such as using linear prediction, 18 using cloud computing technology, 19 using deep learning methods, 20 using wavelet packets, 21 etc. Various challenges in identifying the correct seizure for epilepsy were also studied extensively.…”

Section: Related Workmentioning

confidence: 99%

“…Various studies have been carried out in the prediction and detection of a seizure that can be found in the literature such as, linear prediction, 18 using neural fuzzy methods, 19 using entropy, 20 using wavelet packets, 21 etc. Various challenges in identifying the correct seizure for epilepsy are also studied extensively in the work of Aarabi et al 22 We have succeeded in building a feature set with sixteen features that is ready to feed into a Support Vector Machine and predict the chance of epileptic seizure.…”

Section: Seizure Prediction Using Multiple Features and Svmmentioning

confidence: 99%

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A forecasting tool for prediction of epileptic seizures using a machine learning approach

Asharindavida

Hossain

Thacham

et al. 2018

Concurrency and Computation

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ECG and EEG signals are very helpful in the early diagnosis of epileptic seizures. The research focuses on analysis of ECG and EEG signals applying a deep learning technique to study early prediction of epileptic seizure. Signal processing methods like Empirical Mode Decomposition, spectral analysis, and statistical methods were used. The algorithms were implemented in MATLAB, and the EEG and ECG data were collected from Physiobank and EPILEPSIAE databases.In the window-based analysis of low-frequency spectral area of EEG signals, 78.5% of the cases displayed a significant change as the windows progressed and the onset of seizure was approached. The spectral area of IMF components indicated a possible seizure prediction in 68.9% of the analyzed cases. Considering signals from individual EEG electrodes, the least percentage of seizure prediction was indicated by signals from T4 and F4 electrodes (52.3% and 40.7%, respectively, for spectral peaks and 23.8% and 29.6%, respectively, for spectral area). The results of regression analysis show that prediction of seizures can be possible around 20-30 minutes prior to the actual occurrence of seizures. KEYWORDS Electrocardiogram (ECG), Electroensephalogram (EEG), Empirical Mode Decomposition (EMD), epileptic seizure, machine learning 1 Concurrency Computat Pract Exper. 2020;32:e5111.wileyonlinelibrary.com/journal/cpe

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“…This method is often called as the Auto Regressive method of frequency determination [12]. The Auto Regressive way tends to define the frequency spectra of the signal as 'peaky',(signal which has peaks present for the Power Spectral Density curve at specific frequencies) [12].…”

Section: Linear Prediction Coding (Lpc)mentioning

confidence: 99%

Performance analysis of spectral estimation techniques for steady State Visual Evoked Potentials (SSVEPs) based Brain Computer Interfaces (BCIs)

Dobriyal

Yilmazer

Challoo

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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This paper presents the major challenges and solutions for the Brain Computer Interfaces (BCIs), which are based on Steady State Visual Evoked Potentials (SSVEPs). A BCI utilizes the information transmitted by the brain; there are several methods available for analyzing these brain activities. One of the major challenges in BCI is to remove the noise successfully. Averaging along with Wavelets has been proposed in this paper for de-noising the brain activities. Moreover, three different approaches have been investigated to estimate the frequency spectrum of the brain signals. In addition to the well known Fourier Transform (FT) technique, for spectral estimation, there are many parametric and non parametric techniques for computing the frequency content of a signal. This paper presents a comparison between Fast Fourier Transform (FFT), MUltiple SIgnal Classification (MUSIC) and Linear Predictive Coding (LPC). The effectiveness of different techniques has been studied and the simulation results have shown that MUSIC outperforms the other approaches.

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Linear Prediction Modelling for the Analysis of the Epileptic EEG

Cited by 10 publications

References 4 publications

Epileptic Seizures Prediction Using Deep Learning Techniques

Epileptic Seizures Prediction Using Deep Learning Techniques

A forecasting tool for prediction of epileptic seizures using a machine learning approach

Performance analysis of spectral estimation techniques for steady State Visual Evoked Potentials (SSVEPs) based Brain Computer Interfaces (BCIs)

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