Epileptic Seizure Detection Using Neural Fuzzy Networks

Sadati, N.; Mohseni, Hossein; Maghsoudi, Arash

doi:10.1109/fuzzy.2006.1681772

Cited by 84 publications

(48 citation statements)

References 11 publications

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“…The inclusion criteria for publications considered in the review and full details of the 97 short-listed publications is given in the supplementary material. Based on this review, 65 …”

Section: Methodsmentioning

confidence: 99%

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Optimal features for online seizure detection

Logesparan

Casson

Rodriguez‐Villegas

2012

Med Biol Eng Comput

106

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“…The inclusion criteria for publications considered in the review and full details of the 97 short-listed publications is given in the supplementary material. Based on this review, 65 …”

Section: Methodsmentioning

confidence: 99%

“…-Bounded variation [46,47] and defined here as [74][75][76] and defined here as [50,55,65,74,83] and defined here as …”

Section: Discrete Wavelet Transform-based Featuresmentioning

confidence: 99%

Optimal features for online seizure detection

Logesparan

Casson

Rodriguez‐Villegas

2012

Med Biol Eng Comput

106

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“…Different classifiers have been used in the literature; artificial neural networks [23][24][25][26], nearest neighbor [17], Bayesian [15] fuzzy networks [27].…”

Section: State Of the Artmentioning

confidence: 99%

Automatic seizure detection in SEEG using high frequency activities in wavelet domain

Ayoubian¹,

Lacoma²,

Gotman³

2013

Medical Engineering & Physics

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Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80-500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures.

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“…Ocak, 2008;H. Ocak, 2009;Orhan, Hekim, & Ozer, 2011;Polat & Gunes, 2008b;Sadati, et al, 2006;Subasi, 2007aSubasi, , 2007bSubasi, Alkan, Koklukaya, & Kiymik, 2005;Subasi & Gursoy, 2010;Ubeyli, 2008cUbeyli, , 2009bUbeyli, , 2009cWang, Miao, & Xie, 2011) were often used. Some studies did not use prior information and just used large sets of various features.…”

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%