Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction

Güler, İnan; Übeylı, Elif Derya

doi:10.1016/j.eswa.2004.05.001

Cited by 95 publications

(54 citation statements)

References 30 publications

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“…For each set of detail coefficients (D1-D4) and the approximation wavelet coefficients (A4) we calculate four values: maximum, minimum, mean and standard deviation. That yields a final set of 20 real valued attributes for each class (following the ideas from [6]). …”

Section: Fig 2 Approximate and Detailed Coefficients Of Eeg Signalsmentioning

confidence: 99%

Attribute Selection for EEG Signal Classification Using Rough Sets and Neural Networks

Revett

Szczuka

Jahankhani

et al. 2006

Rough Sets and Current Trends in Computing

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Abstract. This paper describes the application of rough sets and neural network models for classification of electroencephalogram (EEG) signals from two patient classes: normal and epileptic. First, the wavelet transform (WT) was applied to the EEG time series in order to reduce the dimensionality and highlight important features in the data. Statistical measures of the resulting wavelet coefficients were used for the classification task. Employing rough sets, we sought to determine which of the acquired attributes were necessary/informative as predictors of the decision classes. The results indicate that rough sets was able to accurately classify the datasets with an accuracy of almost 100%. The resulting rule sets were small, with an average cardinality of 6. These results were confirmed using standard neural network based classifiers.

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Section: Fig 2 Approximate and Detailed Coefficients Of Eeg Signalsmentioning

confidence: 99%

Attribute Selection for EEG Signal Classification Using Rough Sets and Neural Networks

Revett

Szczuka

Jahankhani

et al. 2006

Rough Sets and Current Trends in Computing

View full text Add to dashboard Cite

show abstract

“…134,135 Design of adaptive neuro-fuzzy systems for the detection of glaucoma, ophthalmic artery stenosis, electrocardiographic changes in patients and facial expression analysis are the applications reported using neuro-fuzzy approach. [136][137][138][139] Fuzzy logic provides a high level framework for approximate reasoning that can appropriately handle both the uncertainty and imprecision in linguistic semantics, model expert heuristics and provides requisite high level organizing principles. The neural network provides self-organizing substrates for low level representation of information with online adaptation capabilities.…”

Section: Neuro-fuzzy Approachesmentioning

confidence: 99%

Fuzzy Expert Systems (FES) for Medical Diagnosis

Sikchi¹,

Sikchi²,

Ali³

2013

IJCA

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Fuzzy logic has proved to be the remarkable tool for building intelligent decision making systems based on the expert's knowledge and observations. This paper reviews the trend in development of FES and application potential over past two and half decades in the medical field, based on the references of 173 articles from 124 journals, several proceedings and web media. In order to investigate the significance of FES for medical diagnosis, the articles are classified into five distinct categories: Reviews and Surveys on Fuzzy Expert Systems in Medical Diagnosis, Applications of Fuzzy Expert Systems in Medical Diagnosis, Methodologies and Modelling of Fuzzy Expert Systems, Neuro-Fuzzy Approaches, Fuzzy Expert System Shells and Frameworks. The development of disease specific applications using FES is observed to be the area of most significant interest of the researchers. The earlier contributions are reviewed, classified, analyzed and suggested the future scope.

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“…It was found that the prediction performances of ANFIS model is better than traditional multiple linear regression model. ANFIS has also been used in the field of science and technology by many researchers (Guler & Ubeyli, 2004;Zaheeruddin & Garima, 2006;Naadimutha et al, 2007;Cakmakci, 2007;Bakhtyar et al, 2008;Wang & Elhag, 2008;Khajeh et al, 2009;Radulovic & Rankovic, 2010;Yan et al, 2010;Ata & Kocyigit, 2010;Sargolzaei & Kianifar, 2010;Yilmaz & Kaynar, 2011;Mohammadi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

Bazzazi¹,

Esmaeili²

2012

Archives of Mining Sciences

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Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R 2 ) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R 2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.Keywords: blasting, backbreak, adaptive neuro-fuzzy inference system, Sangan iron mine Adaptywny system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej (AN-FIS) stanowi potężny narzędzie do rozwiązywania złożonych problemów. Ponieważ model ANFIS może być wykorzystywany do rozwiązywania problemów nieliniowych i umożliwia wygodne przedstawienie problemu w formie: wejście -wyjście, jest idealnym narzędziem do rozwiązywania problemów związanych z prognozowaniem. Pękanie skał w odkrywce jest jednym z niekorzystnych skutków prowadzenia prac strzałowych, powoduje niestabilność ścian, uszkodzenia maszyn i urządzeń, nieodpowiednią fragmentację skał oraz prowadzi do obniżenia efektywności wierceń. W pracy przedstawiono zastosowanie systemu ANFIS do prognozowania pękań skał w kopalni rud żelaza w Sangan (Iran). Działanie modelu zbadano na podstawie wartości błędu średniokwadratowego (RMSE), wariancji (VAF) i współczynnika korelacji Słowa kluczowe: prace strzałowe, pękanie skał, system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej, kopalnia rud żelaza Sangan IntroductionAlthough the main purpose of blasting in open pit mines is rock breakage and finally facilitating in loading operations, the other effects of blasting such as ground vibration, fly rock and backbreak should be considered. Backbreak can be defined as breakage behind the last row of holes (Konya & Walter, 1991). This phenomenon may cause instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling (Bazzazi, 2005). Several factors leading to backbreak have been described by various researchers. Konya and Walter (1991) described some of the causes for backbreak such as excessive burden and stiff benches, long stemming depth on stiff benches and improper timing delay. Gate et al. (2005) believed that a combination of factors in the blasting such as overstemming of the shot holes and short timing delays in the firing sequence may have led to the severe backbreak. Moreover, the adverse geological structure app...

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Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction

Cited by 95 publications

References 30 publications

Attribute Selection for EEG Signal Classification Using Rough Sets and Neural Networks

Attribute Selection for EEG Signal Classification Using Rough Sets and Neural Networks

Fuzzy Expert Systems (FES) for Medical Diagnosis

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

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