Cuneyt Yucelbas

On the evolution of ellipsoidal recognition regions in Artificial Immune Systems

In the mutation procedure of this study, an Ab can go through any of three kinds of mutation (center, length and orientation mutation). An Artificial Immune System was developed based on ellipsoidal recognition regions.Clonal selection principle was utilizes for generating best ellipsoidal regions.Different mutation procedure was applied for the speed of the algorithm.Applications were done on some benchmark data and real-world datasets taken from UCI machine learning repository.Good and promising results have been obtained. Using different shapes of recognition regions in Artificial Immune Systems (AIS) are not a new issue. Especially, ellipsoidal shapes seem to be more intriguing as they have also been used very effectively in other shape space-based classification methods. Some studies have done in AIS through generating ellipsoidal detectors but they are restricted in their detector generating scheme - Genetic Algorithms (GA). In this study, an AIS was developed with ellipsoidal recognition regions by inspiring from the clonal selection principle and an effective search procedure for ellipsoidal regions was applied. Performance evaluation tests were conducted as well as application results on some real-world classification problems taken from UCI machine learning repository were obtained. Comparison with GA was also done in some of these problems. Very effective and comparatively good classification ratios were recorded.

Effect of the Hilbert-Huang transform method on sleep staging

Sleep scoring is performed by examining the recorded electroencephalogram (EEG) and some other signals recorded by a polysomnography (PSG) device. This process is considered more reliable as it is done manually by experts. However, due to the fact that experts may also be mistaken, it has led to an increase in the importance given to automatic sleep staging studies. Many methods have been tested on the signals in order to increase the success of these systems. In this study, an automatic sleep staging system was implemented using the Hilbert-Huang transformation method which is a new time-frequency analysis type. In the study, EEG signals from 5 subjects were used in the sleep laboratory. In the 5-class (Alpha, Beta, Theta, Delta and Spindle bands) applications, the highest classification success was 84.75% as a result of sequential feature selection method.

Elimination of EMG artifacts from EEG signal in sleep staging

Sleep staging is a tiring and time-consuming process for the experts. Thus, attention given to automatic sleep staging studies is increasing gradually. Many factors such as effects of EOG and EKG signals to EEG result in contaminated signals rather than clear recorded signals. EMG contamination to EEG is among that kind of factors. In this study, some filters and Discrete Wavelet Transform based EMG artifact elimination process were evaluated on the performance of sleep staging process. Features were extracted from cleaned EEG signals and subjected to a classifier to conduct sleep staging. By using test classification accuracy as a measure of performance, the method giving highest accuracy was tried to be found. Artificial Neural Networks was used in the applications and Discrete Wavelet Transform was found to be the method giving the highest accuracy.

Detection of the electrode disconnection in sleep signals

Sleep staging process that is performed in sleep laboratories in hospitals has an important role in diagnosing some of the sleep disorders and disturbances which are seen in sleep. And also it is an indispensable method. It is usually performed by a sleep expert through examining during the night of the patients (6-8 hours) recorded Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), electrocardiogram (ECG) and other some signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring studies has come to the fore. However, none of the so far made automatic sleep staging was not accepted by the experts. The most important reason is that the results of the automated systems are not desired accuracy. There are many factors that affecting the accuracy of the systems, such as noise, the inter-channel interference, excessive body movements and disconnection of electrodes. In this study, we examined the written an algorithm to be able to determine to what extent the disconnection of electrodes in EEG signal that obtained one healthy person at the sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University. According to the obtained application results, the electrodes disconnection in EEG signal could be detected maximum of 100% and minimum of 99.12% accuracy. Accordingly, based on the success achieved in the study, this algorithm is thought to contribute positively to the researchers that the work on and will work on sleep staging problems and increase the success of automatic sleep staging systems.