EEG signals classification using the K-means clustering and a multilayer perceptron neural network model

Time series adapted supervised fuzzy discretization: an application to ECG signals

“…According to the selected problem area, the researcher should choose the most suitable method from the several that are available. In this study, a time series adapted neural network model, which can be used without having expertise on data, is proposed by combining a feature extraction method [27,28] and a conventional single layer perceptron classifier. This combination is a new entity like a chemical compound.…”

“…The frequencies of the amplitude values in time series are used as the features of the input signal in some studies [27,28]. In those studies aiming to extract the most meaningful features of a signal, a probability term is used to represent the likelihood of having an amplitude value in any discrete interval.…”

Automated Epileptic Seizure Detection Methods: A Review Study

“…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.…”

“…The classification methods varied from simple threshold (Altunay, Telatar, & Erogul, 2010;Martinez-Vargas, et al, 2011), rule based decisions (Gotman, 1990(Gotman, , 1999, or linear classifiers (Ghosh-Dastidar, Iscan, et al, 2011;Liang, et al, 2010;Subasi & Gursoy, 2010) to ANNs , 2008Mousavi, et al, 2008;Nigam & Graupe, 2004;Srinivasan, et al, 2005Srinivasan, et al, , 2007Tzallas, et al, 2007aTzallas, et al, , 2007bTzallas, et al, , 2009Ubeyli, 2006Ubeyli, , 2009cUbeyli, 2010b) that have a complex shaped decision boundary. Other classification methods have been used using SVMs (Chandaka, et al, 2009;Iscan, et al, 2011;Liang, et al, 2010;Lima, et al, 2010;Subasi & Gursoy, 2010;Ubeyli, 2008a;Ubeyli, 2010a), k-nearest neighbour classifiers (Guo, et al, 2011;Iscan, et al, 2011;Liang, et al, 2010;Orhan, et al, 2011;Tzallas, et al, 2009), quadratic analysis (Iscan, et al, 2011), logistic regression Tzallas, et al, 2009), naive Bayes classifiers (Iscan, et al, 2011;Tzallas, et al, 2009), decision trees (Iscan, et al, 2011;Polat & Gunes, 2007;Tzallas, et al, 2009), Gaussian mixture model …”

Sentiment Analysis With Multilayer Perceptron Using Emoticon Space Model

“…Figure 2. The structure of the MLPNN model [21] In the classification phase, the multilayer perceptron neural network is used. In the classification the tasks are divided as follows and all formulas are taken from reference [22].…”