Browsing by Author "Erogul, Osman"

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Automated Temporal Lobe Epilepsy And Psychogenic Nonepileptic Seizure Patient Discrimination From Multichannel EEG Recordings Using DWT Based Analysis
(2022) Ficici, Cansel; Telatar, Ziya; Erogul, Osman
Psychogenic nonepileptic seizure (PNES) and epileptic seizure resemble each other, behaviorally. This similarity causes misdiagnosis of PNES and epilepsy patients, thus patients suffering from PNES may be treated with antiepileptic drugs which can have various side effects. Furthermore, seizure is diagnosed after time consuming examination of electroencephalography (EEG) recordings realized by the expert. In this study, automated temporal lobe epilepsy (TLE) patient, PNES patient and healthy subject discrimination method from EEG signals is proposed in order to eliminate the misdiagnosis and long inspection time of EEG recordings. Also, this study provides automated approach for TLE interictal and ictal epoch classification, and TLE, PNES and healthy epoch classification. For this purpose, subbands of EEG signals are determined from discrete wavelet transform (DWT), then classification is performed using ensemble classifiers fed with energy feature extracted from the subbands. Experiments are conducted by trying two approaches for TLE, PNES and healthy epoch classification and patient discrimination. Results show that in the TLE, PNES and healthy epoch classification the highest accuracy of 97.2%, sensitivity of 97.9% and specificity of 98.1% were achieved by applying adaptive boosting method, and the highest accuracy of 87.1%, sensitivity of 86.0% and specificity of 93.6% were attained using random under sampling (RUS) boosting method in the TLE patient, PNES patients and the healthy subject discrimination.
Automatic Brain Tumor Detection and Volume Estimation in Multimodal MRI Scans via a Symmetry Analysis
(2023) Ficici, Cansel; Erogul, Osman; Telatar, Ziya; Kocak, Onur; 0000-0002-8240-4046
In this study, an automated medical decision support system is presented to assist physicians with accurate and immediate brain tumor detection, segmentation, and volume estimation from MRI which is very important in the success of surgical operations and treatment of brain tumor patients. In the proposed approach, first, tumor regions on MR images are labeled by an expert radiologist. Then, an automated medical decision support system is developed to extract brain tumor boundaries and to calculate their volumes by using multimodal MR images. One advantage of this study is that it provides an automated brain tumor detection and volume estimation algorithm that does not require user interactions by determining threshold values adaptively. Another advantage is that, because of the unsupervised approach, the proposed study realized tumor detection, segmentation, and volume estimation without using very large labeled training data. A brain tumor detection and segmentation algorithm is introduced that is based on the fact that the brain consists of two symmetrical hemispheres. Two main analyses, i.e., histogram and symmetry, were performed to automatically estimate tumor volume. The threshold values used for skull stripping were computed adaptively by examining the histogram distances between T1- and T1C-weighted brain MR images. Then, a symmetry analysis between the left and right brain lobes on FLAIR images was performed for whole tumor detection. The experiments were conducted on two brain MRI datasets, i.e., TCIA and BRATS. The experimental results were compared with the labeled expert results, which is known as the gold standard, to demonstrate the efficacy of the presented method. The performance evaluation results achieved accuracy values of 89.7% and 99.0%, and a Dice similarity coefficient value of 93.0% for whole tumor detection, active core detection, and volume estimation, respectively.
City Hospitals Model in Biomedical Calibration Service
(2015) Kocak, Onur; Budak, Erdem I.; Beytar, Faruk; Ozgode, Busra; Coruh, Baris; Kocoglu, Arif; Erogul, Osman
Clinical engineering comprise of management of medical technology, medical device maintenance, repair, and calibration which they are bought according to capacity of health institution. In this study, entirely using principles have been given about medical device maintenance, repair, and calibration. A model has been designed which is biomedical calibration production service to new vast city hospitals. The hospitals have high bed capacity and because of that there are more different types of medical devices in their inventory. According to the model has been depicted about separating medical devices too. Besides, process planning has been materialized in biomedical calibration. A new work flow model has been suggested result of evaluating both of calibration and preventive maintenance. Moreover in this study mentioned about laboratory accreditation to international traceability need. Furthermore an offset investment model has been examined to medical device calibrators which they will have bought city hospitals. Urgent actions have detected for all consider authority to the investment model success.
A Clinical Engineering Approach for Design and Management of Central Sterilization Units
(2015) Kocak, Onur; Ozgode, Busra; Kocoglu, Arif; Erogul, Osman; 0000-0002-4640-6570; 0000-0003-4803-5504; AAW-3005-2021; AAF-8122-2020
Central Sterilization Unit (CSU) are the units that performs sterilization of medical devices, instruments and consumables which used in hospitals and these units are planned to provide services within a quality management system and traceability. The numbers of sterilization procedures are carried out in medium and large scale hospitals, this situation can lead to reduced efficiency of the sterilization process have become critical. In this study, using a medium scale hospital as base, planning to work in coordination with the clinical engineering unit the structure of a central sterilization unit that coordinated to work with clinical engineering unit is recommended. The following issues are discussed in detail: architecture of the CSU, departments, staff, process of monitoring, validation and quality cycles. In addition, contributions to the technical efficiency of the sterilization process from biomedical engineers and technicians which are appointed by the clinical engineering units were examined.
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.
Computer-Assisted Diagnosis of Osteoartrithis on Hip Radiographs
(2017) Sahin, Seda; Akata, Emin; Erogul, Osman; Tuncay, Cengiz; Sahin, Orcun; Sanal, Hatice Tuba
Computer-Based Method for Measuring The Anteversion of The Acetabular Component on Pelvic Radiographs
(2017) Sahin, Seda; Akata, Emin; Erogul, Osman; Sanal, Hatice Tuba; Yildiz, Cemil; Tuncay, Cengiz; https://orcid.org/0000-0001-5856-8895; AAF-3988-2021
Detection of PC12 Cell Line Proliferation on AAO Membranes Using Image Processing Techniques
(2015) Uyar, Tansel; Erdamar, Aykut; Aksahin, Mehmet Fevzi; Erogul, Osman; Altuntas, Sevde; Buyukserin, Fatih; 0000-0001-8588-480X; AAA-6844-2019
Evaluation of Total Hip Arthroplasty with Automated Inclination Angle Measurement
(2016) Sahin, Seda; Akata, Emin; Erogul, Osman; Tuncay, Cengiz; Ozkan, Huseyin; https://orcid.org/0000-0001-5856-8895; AAF-3988-2021
A Medical Waste Management Model for Public Private Partnership Hospitals
(2015) Kocak, Onur; Kurtuldu, Huseyin; Akpek, Ali; Kocoglu, Arif; Erogul, Osman; JMC-5224-2023
Today, with developing technologies and expanding health care system, medical waste has reached a fairly large volume. Particularly, the extensive use of disposable medical devices and supplies are among the factors that increase the production of medical waste. Monitoring the processes involving the separation, temporary storage, disposal, and transfer of medical waste is critical in terms of the environment and human health. In this study, the implementation of medical waste collection, separation and classification processes were surveyed in new city hospitals constructed with public-private partnership. The standards for temporarily holding wastes were also discussed. Furthermore, the cost analysis required for the handling and disposal of medical waste was provided. By means of studying the medical waste disposal methods, few suggestions regarding the most appropriate methods and models of offset technology investments for the city hospitals were proposed.
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.
Segmentation of Live and Dead Cells in Tissue Scaffolds
(2018) Uyar, Tansel; Erdamar, Aykut; Aksahin, Mehmet F.; Gumusderelioglu, Menemse; Irmak, Gulseren; Erogul, Osman; U-7861-2018
Image processing techniques are frequently used for extracting quantitative information (cell area, cell size, cell counting, etc.) from different types of microscopic images. Image analysis in the field of cell biology and tissue engineering is time consuming, and requires personal expertise. In addition, evaluation of the results may be subjective. Therefore, computerbased learning / vision-based applications have been developed rapidly in recent years. In this study, images of the viable pre-osteoblastic mouse MC3T3-E1 cells in tissue scaffolds, which was captured from a bone tissue regeneration study, were analyzed by using image processing techniques. Tissue scaffolds were bioprinted from alginate and alginate-hydroxyapatite polymers. Confocal Laser Scanning Microscope images of the tissue scaffolds were processed in the study. Percentages of live and dead cell area in the scaffolds were determined by using image processing techniques at two different time points of the culture.
Temporal Lobe Epilepsy Focus Detection Based on the Correlation Between Brain MR Images and EEG Recordings with a Decision Tree
(DIAGNOSTICS, 2024-11) Ficici, Canse; Telatar, Ziya; Erogul, Osman; Kocak, Onur
Background/Objectives: In this study, a medical decision support system is presented to assist physicians in epileptic focus detection by correlating MRI and EEG data of temporal lobe epilepsy patients. Methods: By exploiting the asymmetry in the hippocampus in MRI images and using voxel-based morphometry analysis, gray matter reduction in the temporal and limbic lobes is detected, and epileptic focus prediction is realized. In addition, an epileptic focus is also determined by calculating the asymmetry score from EEG channels. Finally, epileptic focus detection was performed by associating MRI and EEG data with a decision tree. Results: The results obtained from the proposed algorithm provide 100% overlap with the physician's finding on the EEG data. Conclusions: MRI and EEG correlation in epileptic focus detection was improved compared with physicians. The proposed algorithm can be used as a medical decision support system for epilepsy diagnosis, treatment, and surgery planning.