Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item A Medical Waste Management Model for Public Private Partnership Hospitals(2015) Kocak, Onur; Kurtuldu, Huseyin; Akpek, Ali; Kocoglu, Arif; Erogul, Osman; JMC-5224-2023Today, 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.Item City Hospitals Model in Biomedical Calibration Service(2015) Kocak, Onur; Budak, Erdem I.; Beytar, Faruk; Ozgode, Busra; Coruh, Baris; Kocoglu, Arif; Erogul, OsmanClinical 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.Item 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, OsmanSleep 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.Item 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-8122-2020Central 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.Item 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-6844-2019Item 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-6844-2019In 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.Item Computer-Assisted Diagnosis of Osteoartrithis on Hip Radiographs(2017) Sahin, Seda; Akata, Emin; Erogul, Osman; Tuncay, Cengiz; Sahin, Orcun; Sanal, Hatice TubaItem Segmentation of Live and Dead Cells in Tissue Scaffolds(2018) Uyar, Tansel; Erdamar, Aykut; Aksahin, Mehmet F.; Gumusderelioglu, Menemse; Irmak, Gulseren; Erogul, Osman; U-7861-2018Image 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.Item Automated Temporal Lobe Epilepsy And Psychogenic Nonepileptic Seizure Patient Discrimination From Multichannel EEG Recordings Using DWT Based Analysis(2022) Ficici, Cansel; Telatar, Ziya; Erogul, OsmanPsychogenic 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.