Browsing by Author "Ugur, Tugce Kantar"

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An efficient automatic arousals detection algorithm in single channel EEG
(2019) Ugur, Tugce Kantar; Erdamar, Aykut; 0000-0001-8588-480X; 31046987; AAA-6844-2019
Background and objective: Electroencephalographic arousal is a transient waveform that instantaneously happens in sleep as an inherent component. It has distinctive amplitude and frequency features. However, it is visually difficult to distinguish arousal from the background of the electroencephalogram. This visual scoring is important for brain researches, sleep studies, sleep stage scorings and assessment of sleep disorders. The scoring process is a time-consuming and difficult clinical procedure which is evaluated by sleep experts. It may also have subjective consequences due to the variability of personal expertise of physicians. Conversely, this scoring process can be significantly accelerated with computer-aided automated algorithms. Moreover, reproducible and objective results can be obtained. In this work, we propose a novel algorithm for the automatic detection of electroencephalographic arousals in sleep polysomno-graphic recordings. Methods: The approach uses a well-known time-frequency localization method, the continuous wavelet transform, to identify relevant arousal patterns. Special emphasis was carried out to produce a robust, reliable, fast and artifact tolerant algorithm. In the first part, the electroencephalographic scalogram, the squared magnitude of the continuous wavelet transform, was obtained. The mean and variance of the scalogram coefficients were determined as novel features. Support vector machine was applied as a classifier. Half of the recordings were used for training with five-fold cross-validation and a high accuracy training rate was obtained. Then, the rest of the recordings were used for testing. Results: As a result, the overall sensitivity, specificity, accuracy, and positive predictive value of the algorithm are 94.67%, 99.33%, 98.2%, and 97.93%, respectively. Conclusion: In this paper, we have shown that the electroencephalographic arousal pattern can be characterized by the scalogram in the wavelet domain. The proposed algorithm works with high accuracy, reproducibility and gives objective results without case-specific sensitivity. (C) 2019 Elsevier B.V. All rights reserved.
A Preliminary Study on OSA Severity Levels Detection by Evaluating Speech Signals Nonlinearities With Multi-Class Classification
(2023) Ugur, Tugce Kantar; Yilmaz, Derya; Yildiz, Metin; Yetkin, Sinan
Diagnosis of obstructive sleep apnea (OSA) from speech has become a popular research area in recent years, which can be an alternative way to the application difficulties in polysomnography (PSG). The promising results obtained in our previous study, in which we tried to detect apnea using nonlinear analysis of speech, gave rise to the thought that it is possible to detect OSA and OSA severity by diversifying speech samples and nonlinear features. The principal aim of this study, for the first time in the literature, is to detect the OSA severity levels as mild, moderate, and severe as in the clinic use (multi-class classification) using nonlinear analyses of speech while the patient is awake. In addition, healthy/OSA classification (binary classification) was also carried out. The feature selection method of ANOVA was applied to 336 features (28 voices x 12 features) for each subject, 14 and 5 features were used in multi-class and binary classifications, respectively. As a result of the classifications made with various KNN and SVMs models, the best results were obtained by SVMs in both classifications for OSA severities (with one-vs-all classification scheme and the Gaussian kernel) and OSA detection (with the quadratic kernel) as 82% and 95.1% accuracies, respectively. The proposed study showed that OSA and OSA severity can be determined with the small number of nonlinear features calculated from a few different speech samples, in nearly 15 minutes, consistent with PSG results (simple snorer, mild, moderate, and severe OSA). In conclusion, the highest OSA/healthy classification accuracy rate in the literature was achieved. Furthermore, OSA severity detection in four-class performed quite well as a preliminary study.