Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network

Naghsh-Nilchi, Ahmad Reza; Aghashahi, Mostafa

doi:10.1016/j.bspc.2010.01.004

Cited by 58 publications

(26 citation statements)

References 18 publications

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“…In the combined neural network model [15], where the maximum, minimum, mean and standard deviation were calculated in decomposed wavelet sub bands and a two-level neural network was used, the classification accuracy is found to be 94.83%. NaghshNilchi et al [11] have used a feature vector formed by combining frequency sub-band features (maximum, entropy, average, standard deviation and mobility) and time domain features (standard deviation and complexity measure) for classifying the three classes of EEG signals. When these features were used along with multiple layer perceptron neural networks, an average accuracy of 97.5% has been obtained.…”

Section: Resultsmentioning

confidence: 99%

“…A band-pass filter having a pass band of 0.53-40 Hz (12 dB/oct) was used to select the EEG signal in the desired band. Three datasets corresponding to normal (set A), interictal (set D) and ictal (set E) were used in this work which has also been studied by other researchers [2], [11], [14]- [17].…”

Section: Description Of Datamentioning

confidence: 99%

“…Also various classifiers have been proposed for the classification of EEG signals. Different types of artificial neural networks [9], support vector machine [10], Multiple Layer Perceptron neural network [11], and adaptive neuro fuzzy interference system [12] are few among them. Besides the complex and transforms based ways of feature extraction, a common characteristic of the methods explained above is the use of different types of neural networks which requires training and consumes time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Seizure Detection using Inter Quartile Range

Bedeeuzzaman¹,

Farooq²,

Khan³

2012

IJCA

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The statistical properties of seizure EEG are found to be different from that of the normal EEG. This paper ascertains the efficacy of inter quartile range (IQR), a median based measure of statistical dispersion, as a discriminating feature that can be used for the classification of EEG signals into normal, interictal and ictal classes. IQR along with variance and entropy are calculated for each frame of EEG. To reduce the feature vector size, standard statistical features such as mean, minimum, maximum and standard deviation were evaluated and were given as input to a linear classifier. Without resorting to any kind of transformation, the proposed method reduces the computational complexity and achieves a classification accuracy of 100%. General TermsSignal processing, pattern recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Description Of Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Seizure Detection using Inter Quartile Range

Bedeeuzzaman¹,

Farooq²,

Khan³

2012

IJCA

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“…Entropy is one of the most important features of epileptic states [16]. The energy distribution of a set of wavelet decomposition layers could be described as a probability sequence, the entropy of which reflects the ordering characteristics of a signal.…”

Section: Wavelet Entropy Algorithmmentioning

confidence: 99%

An epileptic focus location method based on ECoG

Tang

Zhang

Cai

et al. 2015

BME

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Abstract. Epilepsy is a neurological disorder characterized by the sudden abnormal discharging of brain neurons that can lead to encephalographic (EEG) abnormalities. In this study, data was obtained from epileptic patients with intracranial depth electrodes and analyzed using wavelet entropy algorithms in order to locate the epileptic foci. Significant increases in the wavelet entropy of the epileptic signals were identified during multiple episodes of clinical seizures. The results indicated that the algorithm was capable of identifying entropy changes in the epileptic sources. Furthermore, the correlations among the electrocorticogram (ECoG) signals of different channels determined using the amplitude-amplitude coupling method verified that the epileptic foci exhibited significantly higher coupling strengths. Thus, cross frequency coupling (CFC) could be an inspiration to energy and signal transitive mode of seizure and, thereby, improve diagnostic processes.

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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%