Automated interictal EEG spike detection using artificial neural networks

Gabor, Andrew J.; Seyal, Masud

doi:10.1016/0013-4694(92)90086-w

Cited by 150 publications

(54 citation statements)

References 5 publications

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“…ANNs have been trained using either raw data (Ko & Chung, 2000;Ozdamar & Kalayci, 1998;Pang, Upton, Shine, & Kamath, 2003;Webber, et al, 1994) or select features (Acir, Oztura, Kuntalp, Baklan, & Guzelis, 2005;Castellaro et al, 2002;Gabor & Seyal, 1992;Liu, Zhang, & Yang, 2002;Pang, et al, 2003;Tzallas, Karvelis, et al, 2006;Webber, et al, 1994) to detect spikes. In the first case, windows of raw EEG data are fed into an ANN.…”

Section: F Methods Based On Artificial Neural Networkmentioning

confidence: 99%

Automated Epileptic Seizure Detection Methods: A Review Study

Tzallas¹,

Tsipouras²,

Tsalikakis³

et al. 2012

Epilepsy - Histological, Electroencephalographic and Psychological Aspects

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Section: F Methods Based On Artificial Neural Networkmentioning

confidence: 99%

Automated Epileptic Seizure Detection Methods: A Review Study

Tzallas¹,

Tsipouras²,

Tsalikakis³

et al. 2012

Epilepsy - Histological, Electroencephalographic and Psychological Aspects

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“…This problem increases the number of false detections that commonly plague all automatic detection systems [4,5]. Due to these reasons, many studies using artificial neural network (ANN) approach have been reported [5][6][7][8][9][10][11][12][13][14]. The ANN based detection systems basically use either of two different input representations: (i) the extracted EEG features or (ii) the raw EEG signal.…”

Section: Introductionmentioning

confidence: 99%

A modified hybrid neural network for pattern recognition and its application to SSW complex in EEG

Acır

2005

Neural Comput & Applic

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In this study, a modified hybrid neural network with asymmetric basis functions is presented for feature extraction of spike and slow wave complexes in electroencephalography (EEG). Feature extraction process has a great importance in all pattern recognition and classification problems. A gradient descent algorithm, indeed a back propagation type, is adapted to the proposed artificial neural network. The performance of the proposed network is measured using a support vector machine classifier fed by features extracted using the proposed neural network. The results show that the proposed neural network model can effectively be used in pattern recognition tasks. In experiments, real EEG data are used.Keywords Gradient descent algorithm AE Feature extraction AE Asymmetric basis functions AE Electroencephalography 2 The modified hybrid neural network and its training algorithmIn this section, we first present the asymmetric basis function, which is the base of the proposed ANN; the N. Acır

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“…The first approach uses pre-extracted parameters as input data to the ANN detection system. Such an approach has been used by many researchers with fairly successful results (21)(22)(23)(24). Generally two types of parameters are used; (1) waveform parameters, such as amplitude, width, slope, and sharpness, which are extracted from epileptic discharges, and (2) context parameters, such as EEG variance, and baseline crossings, which are extracted from the EEG surrounding the epileptiform discharges.…”

Section: Introductionmentioning

confidence: 99%