Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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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 A New Image Segmentation Method for Quantitative Analysis of In Vitro Scratch Assay(2017) Erdamar, Aykut; Yilmaz, Sila; Uyar, Tansel; Darcansoy Iseri, Ozlem; 0000-0001-8588-480X; AAA-6844-2019; U-7861-2018Image processing techniques are frequently used for extracting quantitative information from different types of microscopic images. Particularly, in cell biolop, (cell migration, growth, wound healing etc.), image analysis is time consuming, and requires personal expertise. In addition, evaluation of the results may be subjective. Therefore, computer-based learning/vision-based applications have been developed rapidly in recent years. In this study, micrographs captured during scratch assay for the determination of migration of monolayer adherent carcinoma cells were analyzed by using image processing techniques. Scratch and cell monolayer areas were determined by using tnulti-tresholding and morphological image processing in analysis. In conclusion, scratch area and its border lines were automatically determined, and scratch and cell monolayer areas were calculated.Item Multi-scale classification of single-cell gel electrophoresis assay using deep learning algorithm(2020) Erdamar, Aykut; Aksahin, Mehmet Feyzi; 0000-0001-8588-480X; AAA-6844-2019Structural and functional integrity of deoxyribonucleic acid (DNA) is crucial for the maintenance of hereditary information. However, by-products of cellular metabolism and physical or chemical factors may cause spontaneous DNA damage. The alkaline single-cell gel electrophoresis or comet assay analysis is an easy and reliable method for the determination genotoxic effects of chemical and physical factors. Simply, it is the electrophoretic analysis of intact damaged DNA of a single cell on in a thin layer of agarose gel. The quantitative analysis of the comet assay images is performed manually by an expert researcher. In visual scoring, DNA nuclei are scored as 0, 1, 2, 3, and 4; and the correct scoring is crucial for the determination of the DNA damage. However, visual scoring depends on the professional experience of the researcher and it is a time consuming and exhausting task. Therefore, this evaluation is inevitable to have subjective results. To avoid this subjectivity and to show the effectiveness of deep learning algorithm on cell images, a Convolution Neural Network (CNN) based deep learning method is proposed to classify comet assay images. According to the results, CNN is trained and tested with high accuracy. The results show that CNN algorithm can successfully classify five different scores of comet assay images, and these results can also reduce the subjectivity. (C) 2019 Elsevier Ltd. All rights reserved.Item Quantitative sleep EEG synchronization analysis for automatic arousals detection(2020) Erdamar, Aykut; Aksahin, Mehmet Feyzi; 0000-0001-8588-480X; AAA-6844-2019Background and objective: Electroencephalographic arousals are considered to be the main reason for the interruption of sleep and are visually examined by sleep physicians. Visual scoring of all-night recordings has inter-scorer variability which may lead to subjective results. Hence, we aimed to develop a novel automated method to detect arousals from two electroencephalographic channels in terms of the synchronic events of the right and left hemispheres. Methods: In the context of the occurrence of arousal pattern, the relationship between two synchronic C3-A2 and C4-A1 channels were quantified using by coherence spectrum and mutual information. The power and the ratio values of the sub-bands of the coherence spectrum were selected as the five features. Furthermore, the mutual information value was determined as the sixth feature. The automatic detection performance was evaluated using six features and machine learning techniques, on five different patients' whole-night electroencephalography recordings. The presented method does not include any signal conditioning, pre-processing steps, any manual involvement, meta-rule-based approaches, and some empirical thresholds. Results: The significant increases were found in sub-bands of the coherence spectrum in case of arousal. Moreover, the mutual information of these channels was distinctive during the arousal state. Consequently, the overall accuracy, sensitivity, specificity, and PPV values were achieved as 99.5 %, 99.8 %, 99.6 %, and 99.3 %, respectively with using ensemble bagged tree. Conclusion: The novelty of the present study is the practical determination of the relationship between electroencephalographic synchronization and the occurrence of the arousals between the central regions of the right and left hemispheres. (C) 2020 Elsevier Ltd. All rights reserved.Item An efficient automatic arousals detection algorithm in single channel EEG(2019) Ugur, Tugce Kantar; Erdamar, Aykut; 0000-0001-8588-480X; 31046987; AAA-6844-2019Background and objective: Electroencephalographic arousal is a transient waveform that instantaneously happens in sleep as an inherent component. It has distinctive amplitude and frequency features. However, it is visually difficult to distinguish arousal from the background of the electroencephalogram. This visual scoring is important for brain researches, sleep studies, sleep stage scorings and assessment of sleep disorders. The scoring process is a time-consuming and difficult clinical procedure which is evaluated by sleep experts. It may also have subjective consequences due to the variability of personal expertise of physicians. Conversely, this scoring process can be significantly accelerated with computer-aided automated algorithms. Moreover, reproducible and objective results can be obtained. In this work, we propose a novel algorithm for the automatic detection of electroencephalographic arousals in sleep polysomno-graphic recordings. Methods: The approach uses a well-known time-frequency localization method, the continuous wavelet transform, to identify relevant arousal patterns. Special emphasis was carried out to produce a robust, reliable, fast and artifact tolerant algorithm. In the first part, the electroencephalographic scalogram, the squared magnitude of the continuous wavelet transform, was obtained. The mean and variance of the scalogram coefficients were determined as novel features. Support vector machine was applied as a classifier. Half of the recordings were used for training with five-fold cross-validation and a high accuracy training rate was obtained. Then, the rest of the recordings were used for testing. Results: As a result, the overall sensitivity, specificity, accuracy, and positive predictive value of the algorithm are 94.67%, 99.33%, 98.2%, and 97.93%, respectively. Conclusion: In this paper, we have shown that the electroencephalographic arousal pattern can be characterized by the scalogram in the wavelet domain. The proposed algorithm works with high accuracy, reproducibility and gives objective results without case-specific sensitivity. (C) 2019 Elsevier B.V. All rights reserved.Item 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-6844-2019Objective: 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.