N
Nurettin Acir
Researcher at Bursa Technical University
Publications - 56
Citations - 827
Nurettin Acir is an academic researcher from Bursa Technical University. The author has contributed to research in topics: Adaptive filter & Support vector machine. The author has an hindex of 11, co-authored 50 publications receiving 732 citations. Previous affiliations of Nurettin Acir include Dokuz Eylül University & Niğde University.
Papers
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Journal ArticleDOI
Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks
TL;DR: A three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal is introduced and the overall performance of the system is determined with respect to EVs.
Journal ArticleDOI
Automatic spike detection in EEG by a two-stage procedure based on support vector machines.
Nurettin Acir,Cuneyt Guzelis +1 more
TL;DR: A two-stage procedure based on support vector machines for the automatic detection of epileptic spikes in a multi-channel electroencephalographic signal using a modified non-linear digital filter and a pre-classifier to classify the peaks into two subgroups.
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Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm
TL;DR: Experimental results show that not only the fast L SSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than thestandard backpropagation multilayer perceptron network.
Journal ArticleDOI
A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems
TL;DR: A novel system for ECG beat recognition using Support Vector Machine (SVM) classifier designed by a perturbation method, which recognizes four types of ECG beats obtained from the MIT-BIH database with the accuracy of 96.5% together with discrete cosine transform.
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Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection
TL;DR: A novel system for automatic recognition of auditory brainstem responses (ABR) to detect hearing threshold detection using a support vector machine (SVM) classifier, which is a powerful advanced technique for solving supervised binary classification problem due to its generalization ability.