Assessment of a computer program to detect epileptiform spikes

Hostetler, Wayne E.; Doller, Herbert J.; Homan, Richard W.

doi:10.1016/0013-4694(92)90126-3

Cited by 62 publications

(34 citation statements)

References 41 publications

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“…Ö zdamar et al [23] have reported similarly good results for sensitivity (90%), but selectivity is relatively low (69%). On the other hand, Hostetler et al [24] have achieved a good result for selectivity at 89%, but not for sensitivity, which is at 59%. Although Dingle et al [7] have given a very good result for false detection rate per hour (2%), the sensitivity is very low (14%).…”

Section: Methodsmentioning

confidence: 96%

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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Section: Methodsmentioning

confidence: 96%

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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“…We chose an approach based on event detection for two reasons: first, event detection has constantly been a basic tool for both visual and automatic EEG scoring; then, significant advancements in automatic event detection have been recently achieved. In fact, a number of efficacious methods have been proposed, based on mimetic analysis (Hostetler et al 1992;Gotman and Wang 1992;Dingle et al 1993), mathematical transforms including wavelets (Quiroga and Schürmann 1999;Senhagij and Wendling 2002;Adjouadi et al 2005;Largo and Rosa 2005), adaptive time-frequency approximation through the matching pursuit algorithm (Mallat and Zhang 1993;Durka et al 2004;_ Zygierewicz et al 2005), artificial neural networks (Acir et al 2005;Ventouras et al 2005), fuzzy reasoning (Huupponen et al 2005), polynomial modeling (Bigan and Woolfson 2000), and data mining (Exarchos et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Detection of Signs of Brain Dysfunction in Epileptic Children by Recognition of Transient Changes in the Correlation of Seizure-Free EEG

et al. 2008

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Seizure-free EEG signals recorded from epileptic children were compared with EEG signals recorded from normal children. The comparison was based on the detection of transient events characterized by decrease in the correlation between different traces. For this purpose, a conceptually and mathematically simple method was applied. Two clear and remarkable phenomena, able to quantitatively discriminate between the two groups of subjects, were evidenced, with high statistical significance. In fact, it was observed that: (a) The number of events for the epileptic group was larger; (b) Applying restrictive criteria for event definition, the number of subjects in the epileptic group presenting events was larger. The results support the hypothesis of a decrease in brain correlation in children with epilepsy under treatment. This confirms the efficacy of the EEG signal in evaluating cortical functional differences not visible by visual inspection, independently of the cause (epilepsy or drugs), and demonstrate the specific effectiveness of the analysis method applied.

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“…This makes visual inspection subjective, difficult and time consuming. Visual inspection lacks the ability to anahttp://france.elsevier.com/direct/RBMRET/ ITBM-RBM 27 (2006) [19][20][21][22][23][24] lyze quantitatively so as to explain hidden details of the signal [1,2]. In clinical practice, type of seizure suffered by patient is typically determined by the physician using subjective analysis of the patient or his attendee.…”

Section: Introductionmentioning

confidence: 99%