Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
Browse
2 results
Search Results
Item Continuous Wavelet Transform Based Method For Detection Of Arousal(2017) Kantar, Tugce; Erdamar, Aykut; 0000-0001-8588-480X; AAA-6844-2019Sleeping is a vital biological requirement in the homeostatic mechanism. Sleep disorders can cause personal health problems and life quality deterioration. Transient waveforms (k-complexes, sleep spindles, arousal, etc.) happens instantaneously in sleep, have distinctive structural features, amplitudes and frequencies, and are difficult to distinguish from the background of electroencephalography (EEG) which are called the microstructure of the EEG. Microstructure analysis is important for brain research, sleep studies, sleep stage scorings and assessment of sleep disorders. Detection of arousal, one of these structures, is performed by visual scoring of all night sleep records by an expert sleep physician. In particular, visual analysis of the microstructure is difficult and time-consuming task so it increases error rates in scoring. The aim of this study is automatic detection of arousals from EEG signals. A computer-aided diagnosis system that can detect arousal can give more objective results to the expert sleep experts for diagnosing. In this study, continuous wavelet transform analysis of scored EEG signals was performed and features were obtained As a result, a decision support system algorithm for arousal has been developed using the obtained features and support vector machines. The specificity of the algorithm is 100% and sensitivity is 95.45%.Item Detection of K-complexes in Sleep EEG With Support Vector Machines(2017) Kantar, Tugce; Erdamar, Aykut; 0000-0001-8588-480X; AAA-6844-2019Sleep is a state that can be characterized by the electrical oscillations of nerve cells, where brain activity is more stable than waking. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The determination of the k-complex, which is one of these structures, is performed by visual scoring of all night sleep recordings by expert physicians. For this reason, a decision support system that allows automatic detection of the k-complex can give physicians more objective results in diagnosis. In this study, sleep EEG records scored by a physician were analyzed in different methods from the literature. Three features have been determined that express the k-complex presence and k-complexes were detected using these features and support vector machines. As a result, the performance of the algorithm was evaluated and sensitivity and specificity were determined as 70.83% and 85.29%, respectively.