Elif Derya Übeyli

Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients.

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five 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 saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.

ECG beat classifier designed by combined neural network model

This paper illustrates the use of combined neural network model to guide model selection for classification of electrocardiogram (ECG) beats. The ECG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first level networks were implemented for ECG beats classification using the statistical features as inputs. To improve diagnostic accuracy, the second level networks were trained using the outputs of the first level networks as input data. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified with the accuracy of 96.94% by the combined neural network. The combined neural network model achieved accuracy rates which were higher than that of the stand-alone neural network model.

Combined neural network model employing wavelet coefficients for EEG signals classification

This paper illustrates the use of combined neural network model to guide model selection for classification of electroencephalogram (EEG) signals. The EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the EEG signals classification using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. 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 with the accuracy of 94.83% by the combined neural network. The combined neural network model achieved accuracy rates which were higher than that of the stand-alone neural network model.

ECG beats classification using multiclass support vector machines with error correcting output codes

Abstract 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 electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. The ECG signals were decomposed into time–frequency representations using discrete wavelet transform (DWT) and wavelet coefficients were calculated to represent the signals. The aim of the study is the classification of ECG beats by the combination of wavelet coefficients 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 wavelet coefficients are the features which well represent the ECG signals and the multiclass SVM trained on these features achieved high classification accuracies.

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

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of electrocardiographic changes in patients with partial epilepsy. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Two types of electrocardiogram (ECG) beats (normal and partial epilepsy) were obtained from the MIT-BIH database. 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 electrocardiographic changes 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 electrocardiographic changes in patients with partial epilepsy.

Neural network analysis of internal carotid arterial Doppler signals: predictions of stenosis and occlusion

Abstract Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. Doppler ultrasound has proven to be a valuable technique for investigation of artery conditions. Therefore, Doppler ultrasonography is known as reliable technique, which demonstrates the flow characteristics and resistance of internal carotid arteries in stenosis and occlusion conditions. In this study, internal carotid arterial Doppler signals were obtained from 130 subjects, 45 of them had suffered from internal carotid artery stenosis, 44 of them had suffered from internal carotid artery occlusion and the rest of them had been healthy subjects. Multilayer perceptron neural network employing backpropagation training algorithm was used to predict the presence or absence of internal carotid artery stenosis and occlusion. Spectral analysis of internal carotid arterial Doppler signals was done by Burg autoregressive method for determining the neural network inputs. The network was trained, cross validated and tested with subjects internal carotid arterial Doppler signals. Performance indicators and statistical measures were used for evaluating the neural network. By using the network, the classifications of healthy subjects, subjects having internal carotid artery stenosis, and subjects having internal carotid artery occlusion were done with the accuracy of 95.2, 91.3, and 91.7%, respectively.

Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network

Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. In this study, ophthalmic artery Doppler signals were obtained from 105 subjects, 48 of whom had suffered from ophthalmic artery stenosis. A least-mean squares backpropagation neural network was used to detect the presence or absence of ophthalmic artery stenosis. Spectral analysis of ophthalmic artery Doppler signals was done by the Welch method for determining the neural network inputs. The network was trained, cross validated and tested with subject records from the database. Performance indicators and statistical measures were used for evaluating the neural network. Ophthalmic artery Doppler signals were classified with the accuracy varying from 88.9% to 90.6%.

Theory and Applications of Telemedicine

In this study, telemedicine and the use of advanced telemedicine technologies are explained. Telemedicine is the use of modern telecommunications and information technologies for the provision of clinical care to individuals at a distance, and transmission of information to provide that care. Telemedicine can be used for decision making, remote sensing, and collaborative arrangements for the real-time management of patients at a distance. The use of telecommunications and information technologies in providing health services is determined. Telemedicine is described as combination of topics from the fields of telecommunication, medicine, and informatics. The medical systems infrastructure consisting of the equipment and processes used to acquire and present clinical information and to store and retrieve data are explained in details. The challenges existing in telemedicine development in different countries are given. Technological, political, and professional barriers in applications of telemedicine are defined. An investigation of telemedicine applications in various fields is presented, and enormous impact of telemedicine systems on the future of medicine is determined.

Comparison of eigenvector methods with classical and model-based methods in analysis of internal carotid arterial Doppler signals.

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of arteries in various vascular disease. In this study, internal carotid arterial Doppler signals recorded from 105 subjects were processed by PC-computer using classical, model-based, and eigenvector methods. The classical method (fast Fourier transform), two model-based methods (Burg autoregressive, least-squares modified Yule-Walker autoregressive moving average methods), and three eigenvector methods (Pisarenko, multiple signal classification, and Minimum-Norm methods) were selected for processing internal carotid arterial Doppler signals. Doppler power spectra of internal carotid arterial Doppler signals were obtained using these spectrum analysis techniques. The variations in the shape of the Doppler power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in internal carotid arteries.

Comparison of different classification algorithms in clinical decision‐making

: This paper gives an integrated view of implementing automated diagnostic systems for clinical decision-making. Because of the importance of making the right decision, better classification procedures are necessary for clinical decisions. The major objective of the paper is to be a guide for readers who want to develop an automated decision support system for clinical practice. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and benchmarked for their performance. The performance of the classification algorithms is illustrated on two data sets: the Pima Indians diabetes and the Wisconsin breast cancer. The present research demonstrates that the support vector machines achieved diagnostic accuracies which were higher than those of other automated diagnostic systems.