Ayşegül Güven

Classification of electro-oculogram signals using artificial neural network

Abstract This research is concentrated on the diagnosis of subnormal eye through the analysis of Electrooculography (EOG) signals with the help of Artificial Neural Network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented. The designed classification structure has about 94.1% sensitivity, 93.3% specifity and positive prediction is calculated to be 94.1%. The end results are classified as normal and subnormal eye. Testing results were found to be compliant with the expected results that are derived from the physicians direct diagnosis. The benefit of the system is to assist the physician to make the final decision without hesitation. With the future evolution of this system tested on a more populated subject groups, there is always potential for on-line implementation as an auxiliary diagnostic tool on the Electrophysiology machines.

Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease

Abstract In this study, we developed an expert diagnostic system for the interpretation of the portal vein Doppler signals belong the patients with cirrhosis and healthy subjects using signal processing and Artificial Neural Network (ANN) methods. Power spectral densities (PSD) of these signals were obtained to input of ANN using Short Time Fourier Transform (STFT) method. The four layered Multilayer Perceptron (MLP) training algorithms that we have built had given very promising results in classifying the healthy and cirrhosis. For prediction purposes, it has been presented that Levenberg Marquardt training algorithm of MLP network employing backpropagation works reasonably well. The diagnosis performance of the study shows the advantages of this system: It is rapid, easy to operate, noninvasive and not expensive. This system is of the better clinical application over others, especially for earlier survey of population. The stated results show that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.

Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of optic nerve disease from visual-evoked potential signals

OBJECTIVE This paper presents a new method based on combining principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to diagnose the optic nerve disease from visual-evoked potential (VEP) signals. The aim of this study is to improve the classification accuracy of ANFIS classifier on diagnosis of optic nerve disease from VEP signals. With this aim, a new classifier ensemble based on ANFIS and PCA is proposed. METHODS AND MATERIAL The VEP signals dataset include 61 healthy subjects and 68 patients suffered from optic nerve disease. First of all, the dimension of VEP signals dataset with 63 features has been reduced to 4 features using PCA. After applying PCA, ANFIS trained using three different training-testing datasets randomly with 50-50% training-testing partition. RESULTS The obtained classification results from ANFIS trained separately with three different training-testing datasets are 96.87%, 98.43%, and 98.43%, respectively. And then the results of ANFIS trained with three different training-testing datasets randomly with 50-50% training-testing partition have been combined with three different ways including weighted arithmetical mean that proposed firstly by us, arithmetical mean, and geometrical mean. The classification results of ANFIS combined with three different ways are 98.43%, 100%, and 100%, respectively. Also, ensemble ANFIS has been compared with ANN ensemble. ANN ensemble obtained 98.43%, 100%, and 100% prediction accuracy with three different ways including arithmetical mean, geometrical mean and weighted arithmetical mean. CONCLUSION These results have shown that the proposed classifier ensemble approach based on ANFIS trained with different train-test datasets and PCA has produced very promising results in the diagnosis of optic nerve disease from VEP signals.

Utilization of Discretization method on the diagnosis of optic nerve disease

The optic nerve disease is an important disease that appears commonly in public. In this paper, we propose a hybrid diagnostic system based on discretization (quantization) method and classification algorithms including C4.5 decision tree classifier, artificial neural network (ANN), and least square support vector machine (LSSVM) to diagnose the optic nerve disease from Visual Evoked Potential (VEP) signals with discrete values. The aim of this paper is to investigate the effect of Discretization method on the classification of optic nerve disease. Since the VEP signals are non-linearly-separable, low classification accuracy can be obtained by classifier algorithms. In order to overcome this problem, we have used the Discretization method as data pre-processing. The proposed method consists of two phases: (i) quantization of VEP signals using Discretization method, and (ii) diagnosis of discretized VEP signals using classification algorithms including C4.5 decision tree classifier, ANN, and LSSVM. The classification accuracies obtained by these hybrid methods (combination of C4.5 decision tree classifier-quantization method, combination of ANN-quantization method, and combination of LSSVM-quantization method) with and without quantization strategy are 84.6-96.92%, 94.20-96.76%, and 73.44-100%, respectively. As can be seen from these results, the best model used to classify the optic nerve disease from VEP signals is obtained for the combination of LSSVM classifier and quantization strategy. The obtained results denote that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.

Diagnosis of the macular diseases from pattern electroretinography signals using artificial neural networks

In this paper, we purpose a diagnostic procedure to identify the macular disease from pattern electroretionography (PERG) signals using artificial neural networks (ANN) methods. Multilayer feed forward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented. The designed classification structure has about 96% sensitivity, 100% specifity and correct classification is calculated to be 98%. The end results are classified as Healthy and Diseased. Testing results were found to be compliant with the expected results that are derived from the physicians direct diagnosis, angiography and Arden ratio of electrooculogram (EOG). The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.

Detection of femoral artery occlusion from spectral density of Doppler signals using the artificial neural network

This research is concentrated on the diagnosis of occlusion disease through the analysis of femoral artery Doppler signals with the help of Artificial Neural Network (ANN). Doppler femoral artery signals belong to occlusion patient and healthy subjects were recorded. Afterwards, power spectral densities (PSD) of these signals were obtained using Welch method and Autoregressive (AR) modeling. Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented to these PSD. The designed classification structure has about 98% sensitivity, 97-100% specifity and correct classification is calculated to be 98-99% (for AR modeling and Welch method respectively). The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physicians direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation.

Denoising of Electroretinogram signals using empirical mode decomposition

Clinical Electrophysiologic tests derived from human eyes are the tests that use to review whole visual pathways and they are important for ophthalmology and neuro ophthalmology. Electroretinographies is one of the electrophysiological tests often used to investigate the electrical response of the retinal layers from retinal pigment epithelium up to the occipital cortex. ERG signals have two important amplitudes that are used to diagnose diseases by doctors. These are negative a wave and positive b wave. Implicit times of the a and b waves are also meaningful to diagnose. ERG signals have small amplitudes (about μV). Because of this reason it is significant to separate the signal from the noise and interference that occurs as a result of movement. In this study, we propose using a new technique, called the empirical mode decomposition to denoised ERG responses. The Empirical Mode Decomposition is a signal processing method for analyzing nonlinear and nonstationary signals. ERG signals which are nonstationary signals are decomposed into a series of Intrinsic Mode Functions and then noise and interference are eliminated. Finally ERG signals which have signal to noise ratio less or equal than 10 dB are reconstructed. As a result we successfully obtained denoised ERG signals.

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.

Automatic detection of age-related macular degeneration pathologies in retinal fundus images

Advanced techniques in image processing and analysis are being extensively studied to assist clinical diagnoses. Digital colour retinal fundus images are widely utilised to investigate various eye diseases. In this paper, we describe the detection of optic disc (OD), macula and age-related macular degeneration (ARMD) pathologies of the macular regions in colour fundus images. ARMD causes the loss of central vision in older adults. If the disease is detected early and treated promptly, much of the vision loss can be prevented. Eighty colour retinal fundus images were tested using our proposed algorithm. The Hough transform was employed for OD determination. A fundus coordinate system was established based on the macula location. An ARMD pathology detection methodology using a subtraction process after contrast-limited adaptive histogram equalisation operations was proposed. The accuracies of the automated segmentations of the OD, macula and ARMD pathologies obtained were 100%, 100% and 95.49%, respectively. These results show that our algorithm is a useful tool for detecting ARMD in retinal fundus images. The application of our method may reduce the time needed by ophthalmologists to diagnose ARMD pathology while providing dependable detection precision. Integration of our technique into traditional software could be used in clinical implementations as an aid in disease diagnosis and as a tool for quantitative evaluation of treatment effectiveness.

A Hybrid Automated Detection System Based on Least Square Support Vector Machine Classifier and k-NN Based Weighted Pre-processing for Diagnosing of Macular Disease

In this paper, we proposed a hybrid automated detection system based least square support vector machine (LSSVM) and k-NN based weighted pre-processing for diagnosing of macular disease from the pattern electroretinography (PERG) signals. k-NN based weighted pre-processing is pre-processing method, which is firstly proposed by us. The proposed system consists of two parts: k-NN based weighted pre-processing used to weight the PERG signals and LSSVM classifier used to distinguish between healthy eye and diseased eye (macula diseases). The performance and efficiency of proposed system was conducted using classification accuracy and 10-fold cross validation. The results confirmed that a hybrid automated detection system based on the LSSVM and k-NN based weighted pre-processing has potential in detecting macular disease. The stated results show that proposed method could point out the ability of design of a new intelligent assistance diagnosis system.