A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations

Carvalho, L.; Nassar, Sílvia Modesto; Azevedo, Fernando Mendes de; Carvalho, Hugo J. T. de; Monteiro, Lucas Lese; Rech, Ciciliana Maíla Zilio

doi:10.1590/s0004-282x2008000200007

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…Fuzzy index can distinguish between EEG signals from normal individuals and those from epileptic patients[101] and help in the diagnosis of epilepsy with a sensitivity of 84.9%. [1822] EEG analysis using FIS can also be used to determine the depth of anesthesia. [53] Fuzzy system has also been shown to accurately identify different stages of sleep 84.6% of the times based on EEG findings.…”

Section: Fuzzy Logic In Neurosciencesmentioning

confidence: 99%

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Godil¹,

Shamim²,

Enam³

et al. 2011

Surg Neurol Int

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Background:Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.Methods:This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.Results:The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.Conclusions:In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.

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Section: Fuzzy Logic In Neurosciencesmentioning

confidence: 99%

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Godil¹,

Shamim²,

Enam³

et al. 2011

Surg Neurol Int

View full text Add to dashboard Cite

show abstract

“…The generation of a good knowledge bases for this task is notoriously difficult. This is particularly the case when experts are not readily available [6]. Machine learning techniques (especially rule induction methods) can be of great benefit to this area by providing strategies to automatically extract useful knowledge, given large enough historical datasets [10].…”

Section: Rough Set Theorymentioning

confidence: 99%

Traffic flow breakdown prediction using feature reduction through Rough-Neuro Fuzzy Networks

Affonso

Sassi

Ferreira

2011

The 2011 International Joint Conference on Neural Networks

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The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such São Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and typeRadial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown.

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“…A sequential architecture is used in this work, where Rough Sets and the Neuro Fuzzy Network have distinct functions: Rough Sets selects the critical features, while the Neuro Fuzzy Network generates the surface response input / output, because the Neuro Fuzzy Network has learnability and can adapt itself to the real world [6].…”

Section: Introductionmentioning

confidence: 99%

Rough Neuro-Fuzzy Network Applied to Traffic Flow Breakdown in the City of Sao Paulo

Sassi

Affonso

Ferreira

2011

2011 International Conference on Management and Service Science

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In recent years the market behavior has changed, influenced by several aspects like: increased competitiveness, electronic commerce, environment concerns, among others. The new consumption habits have brought products with a shorter life cycle. These behavior increases the amount of discarded and aimlessly items. Predict the traffic behavior could help to make decision about the routing process, as well as enables the improvement in effectiveness and productivity on its physical distribution. This need motivates the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidence that Artificial Neural Network (ANN) could be use to predict the traffic behavior in a metropolitan area such São Paulo (around 16 million inhabitants). The proposed methodology involves the application Rough-Fuzzy Sets to define inference morphology for insert the behavior of DynamicRouting into a structured rule basis, without human expert aid. The attributes of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule basis. The results show that by making use of the RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown.

show abstract

A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations

Cited by 14 publications

References 14 publications

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Traffic flow breakdown prediction using feature reduction through Rough-Neuro Fuzzy Networks

Rough Neuro-Fuzzy Network Applied to Traffic Flow Breakdown in the City of Sao Paulo

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