EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov expone

Alam, S M Shafiul; Alam, Safeer; Aurangozeb,; TarekShahriar, Syed

doi:10.7763/ijcee.2012.v4.505

Cited by 8 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It decomposes a raw signal into a set of finite number of band-limited functions called intrinsic mode functions (IMFs) possessing properties of the Hilbert transform [20]. As a matter of fact, many research works have been developed using features calculated based on IMFs [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Autoregressive Modeling Based Empirical Mode Decomposition (EMD) for Epileptic Seizures Detection Using EEG Signals

Djemili¹,

Boubchir²

2019

View full text Add to dashboard Cite

Epilepsy is a neurological disorder affecting several millions of humans on earth. Epileptic seizures provoked in major cases by sudden electrical discharges of tremendous brain cells could not be predicted. Hence, automatic seizures detection and classification based on the analysis of electroencephalographic (EEG) signals becomes essential. The purpose of this paper is to propose a new feature extraction method using empirical mode decomposition (EMD) and a multilayer perceptron neural network (MLPNN). The EMD algorithm decomposes a time segment EEG into intrinsic mode functions (IMFs) on which autoregressive (AR) parameters are extracted, combined and fed to the MLPNN classifier. Experimental results carried out on a publicly available dataset, comprising normal, interictal and ictal EEG signals achieved classification accuracy up to 98 %. The outcome of this research is mainly intended to aid practioners in the diagnosis of epileptic portions in the EEG recordings.

show abstract

Section: Introductionmentioning

confidence: 99%

Autoregressive Modeling Based Empirical Mode Decomposition (EMD) for Epileptic Seizures Detection Using EEG Signals

Djemili¹,

Boubchir²

2019

View full text Add to dashboard Cite

show abstract

Automated Detection of Seizure and Nonseizure EEG Signals Using Two Band Biorthogonal Wavelet Filter Banks

Bhati¹,

Pachori

Sharma

et al. 2019

Series in BioEngineering

View full text Add to dashboard Cite

The automated feature identification and classification of nonseizure and seizure electroencephalogram (EEG) is very useful for the diagnosis of epilepsy. In this chapter two band biorthogonal wavelet filter banks are used for classification of nonseizure and seizure EEG signals, and their classification accuracy has been evaluated. The energy or the bispectral phase entropies of the wavelet subbands can be used to discriminate nonseizure and seizure EEG signals. We compare the performance of energy measure and the bispectral phase entropies to discriminate EEG signals. We compare the classification accuracy of thirty biorthogonal filter banks with respect to the regularity orders of the synthesis and analysis of low pass filters and the number of wavelet decompositions. It is found that the energy measure performs better than the bispectral phase entropy for the cases for which the regularity order is greater than or equal to five independent of the wavelet decomposition level. For the fifth and sixth levels of wavelet decomposition, it is found that the energy measure always performed better than the bispectral phase entropy measure independent of the regularity of the filter bank. For the energy measure, the filter banks with higher regularity orders are found to perform better than the filter banks with lower regularity orders at almost all the decomposition levels. However, for the bispectral phase entropy measure, the filter banks with lower regularity orders are

show abstract

An Efficient Adaptive Filter Architecture for Improving the Seizure Detection in EEG Signal

Murali

Chitra²,

Manigandan³

et al. 2015

Circuits Syst Signal Process

View full text Add to dashboard Cite

Cited by 8 publications

References 11 publications

Autoregressive Modeling Based Empirical Mode Decomposition (EMD) for Epileptic Seizures Detection Using EEG Signals

Autoregressive Modeling Based Empirical Mode Decomposition (EMD) for Epileptic Seizures Detection Using EEG Signals

Automated Detection of Seizure and Nonseizure EEG Signals Using Two Band Biorthogonal Wavelet Filter Banks

An Efficient Adaptive Filter Architecture for Improving the Seizure Detection in EEG Signal

Contact Info

Product

Resources

About