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Öğe Analysis of Doppler signals of radial artery for diagnosis of rheumatoid arthritis [Romatoid artrit hastali?i tanisi i?çin radyal arter Doppler sinyal analizi](2010) Özkan A.O.; Kara S.; Salli A.; Güneş S.In this study, Doppler signals received from the radial artery of the right hands of the 40 healthy subjects and 40 patients with rheumatoid arthritis were recorded. Some features of these signals were obtained using Subspace-based MUSIC method which is one of the spectral analysis methods, and the diseased cases were distinguished with Artificial Neural Networks classification method. In MUSIC method, 5,10,15,20 and 25 were used as model degrees. Test procedure was carried out after training with Artificial Neural Networks. Classification accuracy after the test results was 88.75 % for 5 model degrees, 93.75 % for 10 and 25 model degrees, 100 % for 15 model degrees and 92.5 % for 20 model degrees and all models had an average degree of 93.75 % classification accuracy was obtained. The proposed approach has potential to help with the early diagnosis of RA disease for the specialists who study this subject. ©2010 IEEE.Öğe Artificial immune systems and ANN in PMR coding [PMR Kodlamasinda YAPAY Ba?işiklik Si?stemleri? VE YSA](2004) Şahan S.; Ceylan R.; Güneş S.This study aims at introducing a new Artificial Intelligence technique especially beginned to be known newly in our country and models human immune system. Artificial Immune Systems (AIS) are increasing their application areas day by day and coming into existence as a prosperius problem solving technique with their performance on these applications. ABNET is an hybrid system proposed by F. N. De Castro, F. J. Von Zuben and Getúlio A. De Deus Jr. that is a combination of Artificial Neural Networks and immune based metaphors. In this study, the coding in PMR (Private Mobile Radio) is performed with ABNET. To evaluate the results, same coding problem is solved with an ANN system, too. With respect to the results of both system, the applicability of ABNET to real world problems is discussed. © 2004 IEEE.Öğe Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine(2007) Polat K.; Güneş S.Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, we have detected on ECG Arrhythmias using principal component analysis (PCA) and least square support vector machine (LS-SVM). The approach system has two stages. In the first stage, dimension of ECG Arrhythmias dataset that has 279 features is reduced to 15 features using principal component analysis. In the second stage, diagnosis of ECG Arrhythmias was conducted by using LS-SVM classifier. We took the ECG Arrhythmias dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) machine learning database. Classifier system consists of three stages: 50-50% of training-test dataset, 70-30% of training-test dataset and 80-20% of training-test dataset, subsequently, the obtained classification accuracies; 96.86%, 100% ve 100%. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for ECG Arrhythmias disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too. © 2006 Elsevier Inc. All rights reserved.Öğe Examining the effect of time and frequency domain features of EEG, EOG, and Chin EMG signals on sleep staging [EEG, EOG ve Çene EMG sinyallerinden elde edilen zaman ve frekans domeni özelliklerinin uyku evreleme üzerindeki etkisinin i?ncelenmesi](2010) Özşen S.; Güneş S.; Yosunkaya Ş.Sleep staging has an effective role in diagnosing sleep disorders. Sleep staging is generally done by a sleep expert through examining Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections. This type of sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, attention to the automatic sleep staging systems has been begun to increase. In this study, we obtained EEG, EMG and EOG signals of five healthy people in Meram Faculty of medicine to use in sleep staging and extracted 74 features from them. We analyzed the effects of these features on sleep staging. We utilized from the sequential feature selection algorithm and Artificial Neural Networks in this application. We determined which features are more effective in classification of sleep stages and by this way we tried to guide researchers who will use EEG, EMG and EOG features in sleep staging. The highest classification accuracy was obtained as 69.30% with use of four features. ©2010 IEEE.Öğe A new classification method to diagnosis liver disorders: Supervised Artificial Immune System (AIRS) [Karaci?er rahatsizli?i teşhisinde yeni bir siniflama yöntemi: Danişmali Yapay Ba?işiklik Sistemi (AIRS)](2005) Polat K.; Şahan S.; Kodaz H.; Güneş S.Medical diagnosis is very important in medicine. It is necessary to form an efficient and effective computer-based method for decision support in medical analysis. Artificial Immune Systems (AIS), which we can say very new, is an effective and prosperous artificial intelligence area with respect to its problem solving performance. In the beginning, it was formed for helping medical experts to understand the working procedure of immune system in more detail by modeling interactions in immune system. The used medical data was taken from machine learning database of California University in Irvine. In this study, each of data was classified with Artificial Immune Recognition System (AIRS). This application was done by using MATLAB 6.5 programming language. AIRS classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problem it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleverand Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. © 2005 IEEE.
