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Author: search.filters.author.Ardic, SadikHas files: No

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    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.
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    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.