Musa PEKER - Academia.edu (original) (raw)
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Papers by Musa PEKER
Proceedings of the 2016 Sixth International Conference on Advanced Computing & Communication Technologies, 2016
Journal of Medical Systems, 2016
Computer Methods and Programs in Biomedicine, 2016
Neural Computing and Applications, 2016
2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015
Journal of Healthcare Engineering, 2015
Parkinson's disease (PD) is a neu... more Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.
Electronic Journal of Biotechnology, 2015
2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015
2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015
IEEE journal of biomedical and health informatics, Jan 6, 2015
The study reported herein proposes a new method for the diagnosis of epilepsy from electroencepha... more The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out the study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation (DTCWT) at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements-maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks (CVANN). The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity and specificity. The proposed method is tested using a benchmark EEG dataset and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epil...
Journal of medical systems, 2015
The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classificati... more The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed pa...
Journal of Medical Systems, 2014
2011 International Symposium on Innovations in Intelligent Systems and Applications, 2011
Abstract Color detection is generally a primary stage in most of the image processing application... more Abstract Color detection is generally a primary stage in most of the image processing application, if the application is based on the color information, such as road sign detection, face detection, skin color detection, object detection and object tracking etc. As the ...
Proceedings of the 2016 Sixth International Conference on Advanced Computing & Communication Technologies, 2016
Journal of Medical Systems, 2016
Computer Methods and Programs in Biomedicine, 2016
Neural Computing and Applications, 2016
2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015
Journal of Healthcare Engineering, 2015
Parkinson's disease (PD) is a neu... more Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.
Electronic Journal of Biotechnology, 2015
2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015
2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015
IEEE journal of biomedical and health informatics, Jan 6, 2015
The study reported herein proposes a new method for the diagnosis of epilepsy from electroencepha... more The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out the study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation (DTCWT) at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements-maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks (CVANN). The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity and specificity. The proposed method is tested using a benchmark EEG dataset and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epil...
Journal of medical systems, 2015
The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classificati... more The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed pa...
Journal of Medical Systems, 2014
2011 International Symposium on Innovations in Intelligent Systems and Applications, 2011
Abstract Color detection is generally a primary stage in most of the image processing application... more Abstract Color detection is generally a primary stage in most of the image processing application, if the application is based on the color information, such as road sign detection, face detection, skin color detection, object detection and object tracking etc. As the ...