Elif Derya íbeyli

Recurrent neural networks employing Lyapunov exponents for EEG signals classification

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods, non-parametric methods and several neural network models. Unfortunately, there is no theory available to guide model selection. The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) employing Lyapunov exponents trained with Levenberg-Marquardt algorithm on the electroencephalogram (EEG) signals. An approach based on the consideration that the EEG signals are chaotic signals was used in developing a reliable classification method for electroencephalographic changes. This consideration was tested successfully using the non-linear dynamics tools, like the computation of Lyapunov exponents. We explored the ability of designed and trained Elman RNNs, combined with the Lyapunov exponents, to discriminate the EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures). The RNNs achieved accuracy rates which were higher than that of the feedforward neural network models. The obtained results demonstrated that the proposed RNNs employing the Lyapunov exponents can be useful in analyzing long-term EEG signals for early detection of the electroencephalographic changes.

Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals

The aim of the study is classification of the electroencephalogram (EEG) signals by combination of the model-based methods and the least squares support vector machines (LS-SVMs). The LS-SVMs were implemented for classification of two types of EEG signals (set A – EEG signals recorded from healthy volunteers with eyes open and set E – EEG signals recorded from epilepsy patients during epileptic seizures). In order to extract the features representing the EEG signals, the spectral analysis of the EEG signals was performed by using the three model-based methods (Burg autoregressive – AR, moving average – MA, least squares modified Yule–Walker autoregressive moving average – ARMA methods). The present research demonstrated that the Burg AR coefficients are the features which well represent the EEG signals and the LS-SVM trained on these features achieved high classification accuracies. 2009 Elsevier Ltd. All rights reserved.

Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines

A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and multiclass SVM. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the eigenvector methods are the features which well represent the EEG signals and the multiclass SVM trained on these features achieved high classification accuracies. 2007 Elsevier Ltd. All rights reserved.

Multiclass support vector machines for diagnosis of erythemato-squamous diseases

A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for diagnosis of erythemato-squamous diseases. The recurrent neural network (RNN) and multilayer perceptron neural network (MLPNN) were also tested and benchmarked for their performance on the diagnosis of the erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the classifiers learned how to differentiate a new case in the domain. The classifiers were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the multiclass SVM and RNN trained on these features achieved high classification accuracies. 2007 Elsevier Ltd. All rights reserved.

Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals

An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated diagnosis of electrocardiographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Recurrent neural network (RNN) was implemented and used as basis for detection of variabilities of ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the PhysioBank database were classified. Decision making was performed in two stages: computing features which were then input into the RNN and classification using the RNN trained with the Levenberg–Marquardt algorithm. The research demonstrated that the Lyapunov exponents are the features which are well representing the ECG signals and the RNN trained on these features achieved high classification accuracies. 2009 Elsevier Ltd. All rights reserved.

Statistics over features: EEG signals analysis

This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.

A modified mixture of experts network structure for ECG beats classification with diverse features

This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of electrocardiogram (ECG) beats with diverse features. The MME is a modular neural network architecture for supervised learning. Expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. The wavelet coefficients and Lyapunov exponents of the ECG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the ECG signals, were then input into the MME network structure for training and testing purposes. We explored the ability of designed and trained MME network structure, combined with wavelet preprocessing (computing wavelet coefficients) and nonlinear dynamics tools (computing Lyapunov exponents), to discriminate five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network-MLPNN). The proposed MME approach can be useful in classifying long-term ECG signals for early detection of heart diseases/abnormalities.

Features extracted by eigenvector methods for detecting variability of EEG signals

In this paper, we present the expert systems for detecting variability of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, we are looking for better classification procedures for EEG signals. The mixture of experts (ME) and modified mixture of experts (MME) were tested and benchmarked for their performance on the classification of the studied EEG signals. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The inputs of these expert systems composed of diverse or composite features were chosen according to the network structures. The present study was conducted with the purpose of answering the question of whether the expert system with diverse features (MME) or composite feature (ME) improve the capability of classification of the EEG signals. Our research demonstrated that the MME trained on diverse features achieved accuracy rates which were higher than that of the ME.

Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is presented for detection of ophthalmic arterial (OA) disorders. Decision making was performed in two stages: feature extraction using the discrete wavelet transform (DWT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of OA Doppler signals were used as input patterns of the four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of OA disorders were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the OA disorders. 2007 Elsevier Ltd. All rights reserved.

Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks

In this study, a new approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for detection of electrocardiographic changes in patients with partial epilepsy. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electrocardiographic changes in patients with partial epilepsy. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg-Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of training performance and classification accuracies. Receiver operating characteristic (ROC) curves were used to assess the performance of the detection process. The results confirmed that the proposed MLPNN trained with the Levenberg-Marquardt algorithm has potential in detecting the electrocardiographic changes in patients with partial epilepsy.