Wos İndeksli Yayınlar Koleksiyonu

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    Relationship between primary open angle glaucoma and blood pressure
    (2020) Yilmaz, Kerem Can; Gungor, Sirel Sur; Ciftci, Orcun; Akman, Ahmet; Muderrisoglu, Haldun; 0000-0002-9635-6313; 0000-0001-8926-9142; 30650042; AAG-8233-2020; W-5233-2018; AAJ-1331-2021
    Background: Glaucoma is commonly defined as high intra ocular pressure (>= 21 mmHg) with optic neuropathy characterised by progressive loss of retinal ganglion cells which is associated with characteristic structural damage to the optic nerve and visual field loss. There are several studies investigating relation between primary open angle glaucoma (POAG) and both systemic hypertension and especially night hypotension. Our aim was to compare 24-h ambulatory blood pressure variability of patients with glaucoma followed-up in the eye outpatient clinic with that of patients free of glaucoma. Methods: A total of 75 patients were included in the study, 35 in the patient group and 40 in the control group. Both groups were compared for daytime, night-time, and whole day mean systolic and diastolic blood pressure (BP) readings in the ambulatory blood pressure testing. Results: Mean daytime systolic BP of the glaucoma patients was 119.5 +/- 11.6 mmHg, and 128.3 +/- 15.5 mmHg for control group (p = 0.008). The night-time systolic blood pressure, whole day systolic BP, and mean diastolic BP were significantly lower in patients with glaucoma (p = 0.001, p = 0.001, p = 0.028, respectively). In multiple regression analysis, we identified daytime systolic BP, night-time systolic BP, and whole day systolic BP were independent risk factors for developing glaucoma. Conclusion: If the progression of the disease is noticeable in patients with glaucoma at follow-up, night-time hypotension should be ruled out with ambulatory blood pressure and if this is observed medical treatments used by the patients should be reviewed and necessary measures should be taken.
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    Blood pressure prediction from speech recordings
    (2020) Ankishan, Haydar
    The aim of this study is to extract new features to show the relationship between speech recordings and blood pressure (BP). For this purpose, a database consisting of / a / vowels with different BP values under the same room and environment conditions is presented to the literature. Convolutional Neural Networks- Regression (CNN-R), Support Vector Machines- Regression (SVMs-R) and Multi Linear Regression (MLR) are used in this study to predict BP with extracted features. From the experiments, the highest accuracy rates of BP prediction from / a / vowel have been obtained based on Systolic BP values with CNNR. In the study, 89.43 % for MLR, 92.15 % for SVM-R and 93.65 % for CNN-R are obtained when ReliefF has been used. When the root mean square errors (RMSE) are considered, the lowest error value is obtained with CNN-R as RMSE = 0.2355. In conclusion, it can be observed that the proposed feature vector (FVx) shows a relationship between BP and the human voices, and in this direction, it can be used as an FVx in a system that will be developed in order to follow the tension of individuals. (C) 2020 Elsevier Ltd. All rights reserved.