Yazar "Acir, N" seçeneğine göre listele
Listeleniyor 1 - 5 / 5
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A modified hybrid neural network for pattern recognition and its application to SSW complex in EEG(SPRINGER, 2006) Acir, NIn 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.Öğe A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems(PERGAMON-ELSEVIER SCIENCE LTD, 2006) Acir, NIn this paper, we introduce a novel system for ECG beat recognition using Support Vector Machine (SVM) classifier designed by a perturbation method. Three feature extraction methods are comparatively examined in reduced dimensional feature space. The dimension of each feature set is reduced by using perturbation method. If there exist redundant data components in training data set, they can be discarded by analyzing the total disturbance of the SVM output corresponding to the perturbed inputs. Thus, the input dimension size is reduced and network becomes smaller. Algorithm for input dimension reduction is first formulated and then applied to real ECG data for recognition of beat patterns. After the preprocessing of ECG data, four types of ECG beats obtained from the MIT-BIH database are recognized with the accuracy of 96.5% by the proposed system together with discrete cosine transform. (c) 2005 Elsevier Ltd. All rights reserved.Öğe Automated system for detection of epileptiform patterns in EEG by using a modified RBFN classifier(PERGAMON-ELSEVIER SCIENCE LTD, 2005) Acir, NIn this paper, we present a two-stage system based on a modified radial basis function network (RBFN) classifier for an automated detection of epileptiforrn pattern (EP) in an electroencephalographic signal. In the first stage, a discrete perceptron fed by six features are used to classify the peaks into two subgroups: (i) definite non-EPs and (ii) definite EPs and EP-like non-EPs. In the second stage, the peaks falling into the second group are aimed to be separated from each other by a modified RBFN designed by a perturbation method that would function as a post-classifier. If there exist redundant data components in training data set, they can be discarded by analyzing the total disturbance of the RBFN output corresponding to the perturbed inputs. Thus, input dimension size is reduced and network becomes smaller. The classification performance of the system is comparatively evaluated for three different feature sets such as raw EEG data, discrete Fourier transform coefficients, and discrete wavelet transform coefficients. (C) 2005 Elsevier Ltd. All rights reserved.Öğe Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection(PERGAMON-ELSEVIER SCIENCE LTD, 2006) Acir, N; Ozdamar, O; Guzelis, CThis paper presents a novel system for automatic recognition of auditory brainstem responses (ABR) to detect hearing threshold. ABR is an important potential signal for determining objective audiograms. Its detection is usually performed by medical experts with often basic signal processing techniques. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients and a set of discrete wavelet transform (DWT) approximation coefficients are calculated and extracted from signals separately as three different sets of feature vectors. These features are then selected by a modified adaptive method, which mainly supports to the input dimension reduction via selecting the most significant feature components. In the second stage, the feature vectors are classified by a support vector machine (SVM) classifier which is a powerful advanced technique for solving supervised binary classification problem due to its generalization ability. After that the proposed system is applied to real ABR data and it is resulted in a very good sensitivity, specificity and accuracy levels for DCT coefficients such as 99.2%, 94.0% and 96.2%, respectively. Consequently, the proposed system can be used for recognition of ABRs for hearing threshold detection. (c) 2005 Elsevier Ltd. All rights reserved.Öğe Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm(SPRINGER, 2005) Acir, NIn this paper, we present a new system for the classification of electrocardiogram ( ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network.
