Browsing by Author "Firat, Hikmet"

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Comparison of Non - Parametric PSD Detection Methods in the Anaylsis of EEG Signals in Sleep Apnea
(2015) Kocak, Onur; Beytar, Faruk; Firat, Hikmet; Telatar, Ziya; Erogul, Osman
Sleep apnea is characterized by complete cessation of airflowin the mouth and nose for at least 10 seconds and it is a disease that causes significant disruption of sleep patterns. In the absence of treatment, it can lead to serious health problems such as heart attack and stroke. Polysomnography is the gold standard examination methods used in the diagnosis of the disease. In this study, EEG signals obtained from the polysomnography recording are divided into sub-bands and their epochs in pre apnea, intra apnea and post apnea were analyzed. Non-parametric power spectral density (PSD) detection methods (Periodogram, Welch and Multi Taper) applied to the EEG signals were compared.
The Effect of Adding Gender Item to Berlin Questionnaire in Determining Obstructive Sleep Apnea in Sleep Clinics
(2015) Yuceege, Melike; Firat, Hikmet; Sever, Ozlem; Demir, Ahmet; Ardic, Sadik; 25593603
BACKGROUND AND AIM: We aimed to validate the Turkish version of Berlin Questionnaire (BQ) and developped a BQ-gender (BQ-G) form by adding gender component. We aimed to compare the two forms in defining patients with moderate to severe obstructive sleep apnea (OSA) in sleep clinics. METHODS: Four hundred and eighty five consecutive patients, refered to our sleep clinic for snoring, witnessed apnea and/or excessive daytime sleepiness were enrolled to the study. All patients underwent in-laboratory polysomnography (PSG). Patients with sleep efficiency less than 40% and total sleep time less than 4 hours, chronic anxiolitic/sedative drug usage, respiratory tract infection within past two weeks were excluded from the study. All the patients fulfilled BQ. The test and retest for BQ were applied in 15-day interval in 30 patients. RESULTS: Totally 433 patients were enrolled to the study (285 male, 148 female). The mean age of the patients was 47,5 +/- 10.5 (21-79). 180 patients (41.6%) had apnea-hypopnea index (AHI) <= 15, while 253 patients (58,4%) had AHI > 15. The. value was 48-94 and the the truth value was 69-94% for the test-retest procedure. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and area under the curve AUC were 84.2%, 31.7%, 48.7%, 63.4%, and 0.579 in order for BQ and 79.9 %, 51.7%, 63.2%, 69.6%, and 0.652 for BQ-G. CONCLUSION: The results showed that BQ-G is relatively better than BQ in determining moderate to severe OSA in sleep clinics where most of the patients are sleep apneic but both of the tests were found to have insufficient validities in defining moderate to severe OSA in sleep clinics.
Obstructive Sleep Apnea Classification with Artificial Neural Network Based On Two Synchronic Hrv Series
(2015) Aksahin, Mehmet; Erdamar, Aykut; Firat, Hikmet; Ardic, Sadik; Erogul, Osman; 0000-0001-8588-480X; AAA-6844-2019
In the present study, "obstructive sleep apnea (OSA) patients" and "non-OSA patients" were classified into two groups using with two synchronic heart rate variability (HRV) series obtained from electrocardiography (ECG) and photoplethysmography (PPG) signals. A linear synchronization method called cross power spectrum density (CPSD), commonly used on HRV series, was performed to obtain high-quality signal features to discriminate OSA from controls. To classify simultaneous sleep ECG and PPG signals recorded from OSA and non-OSA patients, various feed forward neural network (FFNN) architectures are used and mean relative absolute error (MRAE) is applied on FFNN results to show affectivities of developed algorithm. The FFNN architectures were trained with various numbers of neurons and hidden layers. The results show that HRV synchronization is directly related to sleep respiratory signals. The CPSD of the HRV series can confirm the clinical diagnosis; both groups determined by an expert physician can be 99% truly classified as a single hidden-layer FFNN structure with 0.0623 MRAE, in which the maximum and phase values of the CPSD curve are assigned as two features. In future work, features taken from different physiological signals can be added to define a single feature that can classify apnea without error.
The Quantitative Analysis of Uvulopalatal Flap Surgery
(2017) Erdamar, Aykut; Bayrak, Tuncay; Firat, Hikmet; Mutlu, Murad; Ardic, Sadik; Eroglu, Osman; 0000-0001-8588-480X; AAA-6844-2019
Objective: In this work, a new methodology based on signal processing techniques for the quantitative analysis of uvulopalatal flap surgery is proposed. Clinical assessment studies of uvulopalatal flap surgery are based on not only the physician's examination, but also the patient's subjective feedback. Quantitative and objective evaluation studies are still lacking in the literature. Materials and Methods: Full night sleep records were analyzed for 21 patients before and after the surgery. The proposed algorithm consists of two independent parts. In the first part, the heart rate variability and complexity of the electrocardiogram were calculated. The second part includes calculating the electroencephalogram sub-band energy. Afterwards, the statistical methods were applied in order to determine the correlation of clinical and experimental parameters. Results: The low frequency/high frequency ratio and the sub-band energy of beta wave were significant for the patients having low postoperative delta sleep duration. Moreover, the sub-band energies of both alpha and beta waves, and theta wave were significant for the patients who had high post-operative delta sleep duration and blood oxygen saturation (SaO(2))-parameter. Complexity was significant for the patients with low postoperative respiratory disturbance index and SaO(2) parameter, and respiratory disturbance is correlated with snoring index. Conclusion: Respiratory disturbance index, which is not significant according to the pre- and post-operative clinical findings, was found to be directly related to the complexity feature. The most important result of this work is that the pre-operative complexity feature is correlated with respiratory disturbance and snoring index. This means that complexity feature can be a predictor prior to surgery.
Structural EEG Signal Analysis For Sleep Apnea Classification
(Başkent Üniversitesi Mühendislik Fakültesi, 2024-03-27) Kocak, Onur; Ficici, Cansel; Firat, Hikmet; Telatar, Ziya
Objectives: Diagnosing the sleep apnea can be critical in preventing the person having sleep disorder from unhealthy results. The aim of this study is to obtain a sleep apnea scoring approach by comparing parametric and non-parametric power spectral density (PSD) estimation methods from EEG signals recorded from different brain regions (C4-M1 and O2-M1) for transient signal analysis of sleep apnea patients. Methods: Power Spectral Density (PSD) methods (Burg, Yule-Walker, periodogram, Welch and multi-taper) are examined for the detection of apnea transition states including pre-apnea, intra-apnea and post-apnea together with statistical methods. Results: In the experimental studies, EEG recordings available in the database were analyzed with PSD methods. Results showed that there are statistically significant differences between parametric and non-parametric methods applied for PSD analysis of apnea transition states in delta, theta, alpha and beta bands. Moreover, it was also revealed that PSD of EEG signals obtained from C4-M1 and O2-M1 channels were also found statistically different as proved by classification using the K-nearest neighbour (KNN) method. Conclusions: It was concluded that not only applying different PSD methods, but also EEG signals from different brain regions provided different statistical results in terms of apnea transition states as obtained from KNN classification.