Classification of electroencephalogram signals with combined time and frequency features

İşcan, Zafer; Dokur, Zümray; Demiralp, Tamer

doi:10.1016/j.eswa.2011.02.110

Cited by 174 publications

(84 citation statements)

References 11 publications

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“…In many study, extracted signals are derived at the time of seizure and at normal periods. So, classification performance of these studies were quite high (9,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). In fact, visual assessment might be sufficient at that time.…”

Section: Introductionmentioning

confidence: 96%

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

Öztürk¹,

Türe²,

Kıylıoğlu³

et al. 2018

meandros

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Objective: Electroencephalogram (EEG) signals have been broadly utilized for the diagnosis of epilepsy. Expert physicians must monitor long-term EEG signals that is sometimes difficult and time consuming process for epilepsy diagnosis. In this study, classification performances of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), which are widely used in computer supported epilepsy diagnosis, were compared by using wavelet-based features of extracted from EEG signals which were derived in either normal or inter-ictal periods. In addition, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to determine the effects of dimension reduction on classification success. Materials and Methods:The EEG data were sampled from the EEG laboratory of the Department of Neurology and Clinical Neurophysiology in Adnan Menderes University. Study was approved by local ethics committee with protocol number 2016/873. Ten patients with epilepsy (male -female, age range) and 10 normal (male -female, age range) were the study group. EEG signals of patients with epilepsy were contains only seizure free-epochs. EEG signals were first decomposed into frequency sub-bands by using Discrete Wavelet Transform (DWT) and then some statistical features were calculated from those to classify it's as normal or epileptic. Results: In classification of the EEG signals, it's as normal or epileptic, we achieved %88.9 accuracy rate using SVM with Radial Basis Function (RBF) kernel without dimension reduction. Conclusion: Results showed that, SVM was a powerful tool in classifying EEG signals if it's normal or epileptic.Amaç: Bu çalışmada, epileptik ve epileptik olmayan EEG (Elektroensefelografi) sinyallerinden elde edilen özniteliklerin boyutlarının Temel Bileşenler Analizi (TBA) ve Bağımsız Bileşenler Analizi (BBA) yöntemleri ile indirgenmesinin sınıflandırma başarısı üzerine etkilerinin belirlenmesi ve Doğrusal Ayırma Analizi (DAA) ile Destek Vektör Makinesi (DVM) yöntemlerinin sınıflandırma performanslarının karşılaştırılması amaçlandı. Gereç ve Yöntem: Çalışmaya 10 kontrol ve uzman hekim tarafından epilepsi tanısı konmuş 10 hasta olmak üzere toplam 20 kişi dahil edildi. Epilepsi tanısı konmuş hastalardan alınan EEG kayıtları nöbet geçirmedikleri sırada alınan kayıtlardı. Epileptik

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Section: Introductionmentioning

confidence: 96%

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

Öztürk¹,

Türe²,

Kıylıoğlu³

et al. 2018

meandros

View full text Add to dashboard Cite

show abstract

“…These EEG features represent the information in the time domain. In particular, using the combined time and frequency EEG features has been shown improving the classification of EEG (Iscan, Dokur & Demiralp 2011). Finally, we added the ratio of slow-to-fast activities counted as the ratio of absolute spectral powers in Theta and Alpha bands, increasing the number of features in this group to 12.…”

Section: Problem Statementmentioning

confidence: 99%

Classification of newborn EEG maturity with Bayesian averaging over decision trees

Schetinin

Jakaite

2012

Expert Systems with Applications

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-EEG experts can assess a newborn's brain maturity by visual analysis of age-related patterns in sleep EEG. It is highly desirable to make the results of assessment most accurate and reliable. However, the expert analysis is limited in capability to provide the estimate of uncertainty in assessments. Bayesian inference has been shown providing the most accurate estimates of uncertainty by using Markov Chain Monte Carlo (MCMC) integration over the posterior distribution. The use of MCMC enables to approximate the desired distribution by sampling the areas of interests in which the density of distribution is high. In practice, the posterior distribution can be multimodal, and so that the existing MCMC techniques cannot provide the proportional sampling from the areas of interest. The lack of prior information makes MCMC integration more difficult when a model parameter space is large and cannot be explored in detail within a reasonable time. In particular, the lack of information about EEG feature importance can affect the results of Bayesian assessment of EEG maturity. In this paper we explore how the posterior information about EEG feature importance can be used to reduce a negative influence of disproportional sampling on the results of Bayesian assessment. We found that the MCMC integration tends to oversample the areas in which a model parameter space includes one or more features, the importance of which counted in terms of their posterior use is low. Using this finding, we proposed to cure the results of MCMC integration and then described the results of testing the proposed method on a set of sleep EEG recordings.

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“…Their results showed that the most discriminative features for neonatal seizure detection 1 are morphological based features, such as amplitude, shape and duration of waveforms. In addition, time domain features such as statistical features (Adjouadi et al, 2005), Hjorth's descriptors (Hjorth, 1970), nonlinear features (Kannathal, Acharya, Lim, & Sadasivan, 2005;McSharry, et al, 2002)-correlation dimension (Elger & Lehnertz, 1998), Lyapunov exponent Ubeyli, 2006;Ubeyli, 2010b) and other features obtained from convolution kernels (Adjouadi et al, 2004), eigenvector methods (Naghsh-Nilchi & Aghashahi, 2010 ; Ubeyli, 2008aUbeyli, , 2008bUbeyli, , 2009a, principal component analysis (PCA) (Ghosh-Dastidar, Adeli, & Dadmehr, 2008;Hesse & James, 2007;James & Hesse, 2005;Polat & Gunes, 2008a;Subasi & Gursoy, 2010), ICA (Hesse & James, 2007;James & Hesse, 2005;Subasi & Gursoy, 2010), crosscorrelation function (Chandaka, Chatterjee, & Munshi, 2009;Iscan, et al, 2011), and entropy (Guo, Rivero, Dorado, et al, 2010;Kannathal, Choo, Acharya, & Sadasivan, 2005;Liang, Wang, & Chang, 2010;Naghsh-Nilchi & Aghashahi, 2010 ;H. Ocak, 2009;Srinivasan, Eswaran, & Sriraam, 2007;Wang, et al, 2011) have been proposed to characterize the EEG signal.…”

Section: Automated Epileptic Seizure Analysismentioning

confidence: 99%

“…Just as a wide variety of features has been used, an equally varied set of classification methods can be found in the literature. The classification methods varied from simple threshold (Altunay, Telatar, & Erogul, 2010;Martinez-Vargas, et al, 2011), rule based decisions (Gotman, 1990(Gotman, , 1999, or linear classifiers (Ghosh-Dastidar, Iscan, et al, 2011;Liang, et al, 2010;Subasi & Gursoy, 2010) to ANNs , 2008Mousavi, et al, 2008;Nigam & Graupe, 2004;Srinivasan, et al, 2005Srinivasan, et al, , 2007Tzallas, et al, 2007aTzallas, et al, , 2007bTzallas, et al, , 2009Ubeyli, 2006Ubeyli, , 2009cUbeyli, 2010b) that have a complex shaped decision boundary. Other classification methods have been used using SVMs (Chandaka, et al, 2009;Iscan, et al, 2011;Liang, et al, 2010;Lima, et al, 2010;Subasi & Gursoy, 2010;Ubeyli, 2008a;Ubeyli, 2010a), k-nearest neighbour classifiers (Guo, et al, 2011;Iscan, et al, 2011;Liang, et al, 2010;Orhan, et al, 2011;Tzallas, et al, 2009), quadratic analysis (Iscan, et al, 2011), logistic regression Tzallas, et al, 2009), naive Bayes classifiers (Iscan, et al, 2011;Tzallas, et al, 2009), decision trees (Iscan, et al, 2011;Polat & Gunes, 2007;…”

Section: Automated Epileptic Seizure Analysismentioning

confidence: 99%

“…For rhythmic discharges, fast Fourier transform based (Polat & Gunes, 2007, 2008a, 2008b, frequency domain Chua, Chandran, Acharya, & Lim, 2008;Gabor, 1998;Iscan, Dokur, & Tamer, 2011;Mousavi, Niknazar, & Vahdat, 2008;Murro et al, 1991;Nigam & Graupe, 2004;Sadati, Mohseni, & Magshoudi, 2006;Srinivasan, Eswaran, & Sriraam, 2005;Ubeyli, 2010a), time-frequency based (Martinez-Vargas, Avendano-Valencia, Giraldo, & Castellanos-Dominguez, 2011;Subasi & Gursoy, 2010;Tzallas, et al, 2007aTzallas, et al, , 2007bTzallas, et al, , 2009 or wavelet based features , 2007Guo, Rivero, Dorado, Rabunal, & Pazos, 2010;Guo, Rivero, Seoane, & Pazos, 2009;Kiymik, Subasi, & Ozcalik, 2004;Lima, Coelho, & Eisencraft, 2010;H. Ocak, 2008;H.…”

Section: Automated Epileptic Seizure Analysismentioning

confidence: 99%

See 1 more Smart Citation

Automated Epileptic Seizure Detection Methods: A Review Study

Tzallas¹,

Tsipouras²,

Tsalikakis³

et al. 2012

Epilepsy - Histological, Electroencephalographic and Psychological Aspects

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Classification of electroencephalogram signals with combined time and frequency features

Cited by 174 publications

References 11 publications

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

Classification of newborn EEG maturity with Bayesian averaging over decision trees

Automated Epileptic Seizure Detection Methods: A Review Study

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