The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform

Kiyoshi Yaoeda - One of the best experts on this subject based on the ideXlab platform.

  • association between a relative afferent pupillary defect using pupillography and inner retinal atrophy in Optic Nerve Disease
    Clinical Ophthalmology, 2015
    Co-Authors: Go Takizawa, Fumiatsu Maeda, Syunsuke Araki, Katsutoshi Goto, Yoshiaki Ieki, Junichi Kiryu, Atsushi Miki, Kiyoshi Yaoeda
    Abstract:

    The aim of this study was to compare the asymmetrical light reflex of the control subjects and patients with Optic Nerve Disease and to evaluate the relationships among the relative afferent pupillary defect (RAPD), visual acuity (VA), central critical fusion frequency (CFF), ganglion cell complex thickness (GCCT), and circumpapillary retinal Nerve fiber layer thickness (cpRNFLT) using spectral-domain Optical coherence tomography.Using a pupillography device, the RAPD scores from 15 patients with unilateral Optic Nerve Disease and 35 control subjects were compared. The diagnostic accuracy of the RAPD amplitude and latency scores was compared using the area under the receiver operating characteristic curve. Thereafter, we assessed the relationships among the RAPD scores, VA, central CFF, GCCT, and cpRNFLT.The average RAPD amplitude score in patients with Optic Nerve Disease was significantly higher than that of the control subjects (P<0.001). The average RAPD latency score in patients with Optic Nerve Disease was significantly higher than that of the control subjects (P=0.001). The area under the receiver operating characteristic curve for the RAPD amplitude score was significantly higher than that for the latency score (P=0.010). The correlation coefficients for the RAPD amplitude and latency scores were 0.847 (P<0.001) and 0.874 (P<0.001) for VA, -0.868 (P<0.001) and -0.896 (P<0.001) for central CFF, -0.593 (P=0.020) and -0.540 (P=0.038) for GCCT, and -0.267 (P=0.337) and -0.228 (P=0.413) for cpRNFLT, respectively.Our results suggest that pupillography is useful for detecting Optic Nerve Disease.

  • Association between a relative afferent pupillary defect using pupillography and inner retinal atrophy in Optic Nerve Disease
    Clinical Ophthalmology, 2015
    Co-Authors: Go Takizawa, Fumiatsu Maeda, Syunsuke Araki, Katsutoshi Goto, Yoshiaki Ieki, Junichi Kiryu, Atsushi Miki, Kiyoshi Yaoeda
    Abstract:

    The aim of this study was to compare the asymmetrical light reflex of the control subjects and patients with Optic Nerve Disease and to evaluate the relationships among the relative afferent pupillary defect (RAPD), visual acuity (VA), central critical fusion frequency (CFF), ganglion cell complex thickness (GCCT), and circumpapillary retinal Nerve fiber layer thickness (cpRNFLT) using spectral-domain Optical coherence tomography.Using a pupillography device, the RAPD scores from 15 patients with unilateral Optic Nerve Disease and 35 control subjects were compared. The diagnostic accuracy of the RAPD amplitude and latency scores was compared using the area under the receiver operating characteristic curve. Thereafter, we assessed the relationships among the RAPD scores, VA, central CFF, GCCT, and cpRNFLT.The average RAPD amplitude score in patients with Optic Nerve Disease was significantly higher than that of the control subjects (P

Ayşegül Güven - One of the best experts on this subject based on the ideXlab platform.

  • Utilization of Discretization method on the diagnosis of Optic Nerve Disease
    Computer Methods and Programs in Biomedicine, 2008
    Co-Authors: Kemal Polat, Ayşegül Güven, Sadik Kara, Salih Güneş
    Abstract:

    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.

  • Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of Optic Nerve Disease from visual-evoked potential signals
    Artificial intelligence in medicine, 2008
    Co-Authors: Bayram Akdemir, Kemal Polat, Ayşegül Güven, Sadik Kara, Salih Güneş
    Abstract:

    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.

  • The effect of generalized discriminate analysis (GDA) to the classification of Optic Nerve Disease from VEP signals
    Computers in biology and medicine, 2007
    Co-Authors: Ayşegül Güven, Kemal Polat, Sadik Kara, Salih Güneş
    Abstract:

    In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of Optic Nerve Disease from visual evoke potentials (VEP) signals. The GDA method has been used as a pre-processing step prior to the classification process of Optic Nerve Disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of Optic Nerve Disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of Optic Nerve Disease from VEP signals.

  • Neural Network-Based Diagnosing for Optic Nerve Disease from Visual-Evoked Potential
    Journal of medical systems, 2007
    Co-Authors: Sadik Kara, Ayşegül Güven
    Abstract:

    In this paper, we purpose a diagnostic procedure to identify the Optic Nerve Disease from visual evoked potential (VEP) signals using an Artificial Neural Network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented. The correct classification rate was 96.87% for subjects having Optic Nerve Disease and 96.66% for healthy subjects. 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 physician's direct diagnosis, angiography, VEP and pattern electroretinography. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.

  • classification of macular and Optic Nerve Disease by principal component analysis
    Computers in Biology and Medicine, 2007
    Co-Authors: Sadik Kara, Ayşegül Güven, Semra Icer
    Abstract:

    In this study, pattern electroretinography (PERG) signals were obtained by electrophysiological testing devices from 70 subjects. The group consisted of Optic Nerve and macular Diseases subjects. Characterization and interpretation of the physiological PERG signal was done by principal component analysis (PCA). While the first principal component of data matrix acquired from Optic Nerve patients represents 67.24% of total variance, the first principal component of the macular patients data matrix represents 76.81% of total variance. The basic differences between the two patient groups were obtained with first principal component, obviously. In addition, the graphic of second principal component vs. first principal component of Optic Nerve and macular subjects was analyzed. The two patient groups were separated clearly from each other without any hesitation. This research developed an auxiliary system for the interpretation of the PERG signals. The stated results show that the use of PCA of physiological waveforms is presented as a powerful method likely to be incorporated in future medical signal processing.

Go Takizawa - One of the best experts on this subject based on the ideXlab platform.

  • association between a relative afferent pupillary defect using pupillography and inner retinal atrophy in Optic Nerve Disease
    Clinical Ophthalmology, 2015
    Co-Authors: Go Takizawa, Fumiatsu Maeda, Syunsuke Araki, Katsutoshi Goto, Yoshiaki Ieki, Junichi Kiryu, Atsushi Miki, Kiyoshi Yaoeda
    Abstract:

    The aim of this study was to compare the asymmetrical light reflex of the control subjects and patients with Optic Nerve Disease and to evaluate the relationships among the relative afferent pupillary defect (RAPD), visual acuity (VA), central critical fusion frequency (CFF), ganglion cell complex thickness (GCCT), and circumpapillary retinal Nerve fiber layer thickness (cpRNFLT) using spectral-domain Optical coherence tomography.Using a pupillography device, the RAPD scores from 15 patients with unilateral Optic Nerve Disease and 35 control subjects were compared. The diagnostic accuracy of the RAPD amplitude and latency scores was compared using the area under the receiver operating characteristic curve. Thereafter, we assessed the relationships among the RAPD scores, VA, central CFF, GCCT, and cpRNFLT.The average RAPD amplitude score in patients with Optic Nerve Disease was significantly higher than that of the control subjects (P<0.001). The average RAPD latency score in patients with Optic Nerve Disease was significantly higher than that of the control subjects (P=0.001). The area under the receiver operating characteristic curve for the RAPD amplitude score was significantly higher than that for the latency score (P=0.010). The correlation coefficients for the RAPD amplitude and latency scores were 0.847 (P<0.001) and 0.874 (P<0.001) for VA, -0.868 (P<0.001) and -0.896 (P<0.001) for central CFF, -0.593 (P=0.020) and -0.540 (P=0.038) for GCCT, and -0.267 (P=0.337) and -0.228 (P=0.413) for cpRNFLT, respectively.Our results suggest that pupillography is useful for detecting Optic Nerve Disease.

  • Association between a relative afferent pupillary defect using pupillography and inner retinal atrophy in Optic Nerve Disease
    Clinical Ophthalmology, 2015
    Co-Authors: Go Takizawa, Fumiatsu Maeda, Syunsuke Araki, Katsutoshi Goto, Yoshiaki Ieki, Junichi Kiryu, Atsushi Miki, Kiyoshi Yaoeda
    Abstract:

    The aim of this study was to compare the asymmetrical light reflex of the control subjects and patients with Optic Nerve Disease and to evaluate the relationships among the relative afferent pupillary defect (RAPD), visual acuity (VA), central critical fusion frequency (CFF), ganglion cell complex thickness (GCCT), and circumpapillary retinal Nerve fiber layer thickness (cpRNFLT) using spectral-domain Optical coherence tomography.Using a pupillography device, the RAPD scores from 15 patients with unilateral Optic Nerve Disease and 35 control subjects were compared. The diagnostic accuracy of the RAPD amplitude and latency scores was compared using the area under the receiver operating characteristic curve. Thereafter, we assessed the relationships among the RAPD scores, VA, central CFF, GCCT, and cpRNFLT.The average RAPD amplitude score in patients with Optic Nerve Disease was significantly higher than that of the control subjects (P

Sadik Kara - One of the best experts on this subject based on the ideXlab platform.

  • Utilization of Discretization method on the diagnosis of Optic Nerve Disease
    Computer Methods and Programs in Biomedicine, 2008
    Co-Authors: Kemal Polat, Ayşegül Güven, Sadik Kara, Salih Güneş
    Abstract:

    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.

  • Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of Optic Nerve Disease from visual-evoked potential signals
    Artificial intelligence in medicine, 2008
    Co-Authors: Bayram Akdemir, Kemal Polat, Ayşegül Güven, Sadik Kara, Salih Güneş
    Abstract:

    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.

  • The effect of generalized discriminate analysis (GDA) to the classification of Optic Nerve Disease from VEP signals
    Computers in biology and medicine, 2007
    Co-Authors: Ayşegül Güven, Kemal Polat, Sadik Kara, Salih Güneş
    Abstract:

    In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of Optic Nerve Disease from visual evoke potentials (VEP) signals. The GDA method has been used as a pre-processing step prior to the classification process of Optic Nerve Disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of Optic Nerve Disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of Optic Nerve Disease from VEP signals.

  • Neural Network-Based Diagnosing for Optic Nerve Disease from Visual-Evoked Potential
    Journal of medical systems, 2007
    Co-Authors: Sadik Kara, Ayşegül Güven
    Abstract:

    In this paper, we purpose a diagnostic procedure to identify the Optic Nerve Disease from visual evoked potential (VEP) signals using an Artificial Neural Network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented. The correct classification rate was 96.87% for subjects having Optic Nerve Disease and 96.66% for healthy subjects. 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 physician's direct diagnosis, angiography, VEP and pattern electroretinography. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.

  • classification of macular and Optic Nerve Disease by principal component analysis
    Computers in Biology and Medicine, 2007
    Co-Authors: Sadik Kara, Ayşegül Güven, Semra Icer
    Abstract:

    In this study, pattern electroretinography (PERG) signals were obtained by electrophysiological testing devices from 70 subjects. The group consisted of Optic Nerve and macular Diseases subjects. Characterization and interpretation of the physiological PERG signal was done by principal component analysis (PCA). While the first principal component of data matrix acquired from Optic Nerve patients represents 67.24% of total variance, the first principal component of the macular patients data matrix represents 76.81% of total variance. The basic differences between the two patient groups were obtained with first principal component, obviously. In addition, the graphic of second principal component vs. first principal component of Optic Nerve and macular subjects was analyzed. The two patient groups were separated clearly from each other without any hesitation. This research developed an auxiliary system for the interpretation of the PERG signals. The stated results show that the use of PCA of physiological waveforms is presented as a powerful method likely to be incorporated in future medical signal processing.

Salih Güneş - One of the best experts on this subject based on the ideXlab platform.

  • Utilization of Discretization method on the diagnosis of Optic Nerve Disease
    Computer Methods and Programs in Biomedicine, 2008
    Co-Authors: Kemal Polat, Ayşegül Güven, Sadik Kara, Salih Güneş
    Abstract:

    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.

  • Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of Optic Nerve Disease from visual-evoked potential signals
    Artificial intelligence in medicine, 2008
    Co-Authors: Bayram Akdemir, Kemal Polat, Ayşegül Güven, Sadik Kara, Salih Güneş
    Abstract:

    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.

  • The effect of generalized discriminate analysis (GDA) to the classification of Optic Nerve Disease from VEP signals
    Computers in biology and medicine, 2007
    Co-Authors: Ayşegül Güven, Kemal Polat, Sadik Kara, Salih Güneş
    Abstract:

    In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of Optic Nerve Disease from visual evoke potentials (VEP) signals. The GDA method has been used as a pre-processing step prior to the classification process of Optic Nerve Disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of Optic Nerve Disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of Optic Nerve Disease from VEP signals.