Browsing by Author "Erdamar, Aykut"

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Analysis of Heart Diseases from ECG Signal
(2014) Kantar, Tugce; Koseoglu, Ovul; Erdamar, Aykut; https://orcid.org/0000-0001-8588-480X; AAA-6844-2019
At the present time, the main method used in the diagnosis of heart disease is the electrocardiography (ECG). The purpose of this study is design a decision support algorithm which automatically detect the normal sinus rhythm or other pathologies. The improved algorithm will provide support to the doctor can be also used for educational purposes. Within the scope of this study, with the design of the rule based algorithm which automatically detect a normal sinus rhythm and non normal sinus rhythm, in total it can detect eight pathologies. In maincode there are thirteen functions that are used for diagnose eight different ECG pathology automatically. Higher success is being anticipated in future for the prediction power of the developed method with continuing research on the matter.
Automatic Classification of Respiratory Sounds During Sleep
(2018) Kilic, Erkin; Erdamar, Aykut
Sounds like snoring, coughing, sneezing, whistling, which have different acoustic properties, can emerge involuntarily during the sleep. These sounds may affect negatively the sleep quality of the other people in the same environment, just as it may affect directly the sleep quality. To increase the sleep quality, these sounds should be recorded and evaluated by a sleep expert. This is an expertise required process that can be time-consuming and subjective results. In this study, it has been aimed that developing a computer-aided diagnosing algorithm which will classify the sounds emerging during the sleep automatically with high accuracy by analyzing the records in a fast and effective way to help the sleep expert to diagnose. The mathematical features have been obtained in frequency and time domains by applying continuous wavelet transform for the different type of sounds. Support vector machine used as a classifier. 390 and 449 segments were used for training and testing respectively. As a result of the study, six different parameters which are exhalation, simple snoring, high frequency duplex snoring, low frequency duplex snoring, triplex snoring and coughing were classified with 96.44% accuracy rate.
Automatic Detection of Sleep Spindles With Quadratic Discriminant Analysis
(2018) Kokerer, Sila Turku; Celik, Elif Oyku; Kantar, Tugce; Erdamar, Aykut
Sleep, is in the event of temporary loss of consciousness. Sleep and wakefulness causes some different kind of potential changes in brain. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The main objective of this study is to develop a method to detect sleep spindle, which is one of these structures, with high-accuracy. Sleep spindles that require the expertise to determine visually, is a process that can be time-consuming and subjective results. In this study, electroencephalography records, scored by expert sleep physicians, were analyzed by different methods. Two features have been determined that express the presence of sleep spindle. Sleep spindles were detected by these features and quadratic discriminant analysis. As a result, the performance of the algorithm was evaluated and sensitivity, specificity and accuracy were determined as 95.74%, 98.08% and 97.76%, respectively.
Classification of Heart Sound Recordings With Continuous Wavelet Transform Based Algorithm
(2018) Karaca, Busra Kubra; Oltu, Burcu; Kantar, Tugce; Kilic, Erkin; Aksahin, Mehmet Feyzi; Erdamar, Aykut
Cardiovascular diseases are the major cause of death in the world. Early diagnosis of heart diseases provide an effective treatment. Heart diseases can be diagnosed using data obtained from heart sounds. Heart sounds are listened by a physician with auscultation method and the disease diagnosis can vary depending on the physician's experience and hearing ability. For this reason, automatic detection of anomalies in heart sounds can give more objective results. In this study, features were obtained by processing phonocardiogram signals taken from Physionet database. The heart sounds are classified as normal and abnormal using these features and the k - nearest neighbor method. As a result, sensitivity, specificity and accuracy were determined as 100%, 96.1% and 98.2%, respectively.
Continuous Wavelet Transform Based Method For Detection Of Arousal
(2017) Kantar, Tugce; Erdamar, Aykut; 0000-0001-8588-480X; AAA-6844-2019
Sleeping is a vital biological requirement in the homeostatic mechanism. Sleep disorders can cause personal health problems and life quality deterioration. Transient waveforms (k-complexes, sleep spindles, arousal, etc.) happens instantaneously in sleep, have distinctive structural features, amplitudes and frequencies, and are difficult to distinguish from the background of electroencephalography (EEG) which are called the microstructure of the EEG. Microstructure analysis is important for brain research, sleep studies, sleep stage scorings and assessment of sleep disorders. Detection of arousal, one of these structures, is performed by visual scoring of all night sleep records by an expert sleep physician. In particular, visual analysis of the microstructure is difficult and time-consuming task so it increases error rates in scoring. The aim of this study is automatic detection of arousals from EEG signals. A computer-aided diagnosis system that can detect arousal can give more objective results to the expert sleep experts for diagnosing. In this study, continuous wavelet transform analysis of scored EEG signals was performed and features were obtained As a result, a decision support system algorithm for arousal has been developed using the obtained features and support vector machines. The specificity of the algorithm is 100% and sensitivity is 95.45%.
Detection of Epilepsy Disease From EEG Signals With Artificial Neural Networks
(2016) Ozkan, Cansu; Dogan, Seda; Kantar, Tugce; Aksahin, Mehmet Feyzi; Erdamar, Aykut; 0000-0001-8588-480X; AAA-6844-2019
The diagnosis of the epilepsy diseases are made by physicians with analyzing the electroencephalography (EEG) records. The epilepsy diseases can be determined with observing the main properties of before and on-time seizure signals in time and frequency domain. Physicians are evaluating the results after some necessary scoring on EEG records. However, this evaluation is specialistic, time consuming processes and also may subjective results. At this point, to allow detection of epilepsy diseases, a decision support system can give more objective results to the physicians for diagnosing. The subject of the study is automatically diagnosing the epilepsy diseases on EEG signals. In the proposed study, analyses of EEG signals in time and frequency domain were done and features of diseases were obtained. As a result, using artificial neural network (ANN) and obtained features, a decision support system is realized to diagnose the epilepsy. The specificity and the sensitivity of the algorithm are 94% and 66% respectively.
Detection of K-complexes in Sleep EEG With Support Vector Machines
(2017) Kantar, Tugce; Erdamar, Aykut; 0000-0001-8588-480X; AAA-6844-2019
Sleep is a state that can be characterized by the electrical oscillations of nerve cells, where brain activity is more stable than waking. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The determination of the k-complex, which is one of these structures, is performed by visual scoring of all night sleep recordings by expert physicians. For this reason, a decision support system that allows automatic detection of the k-complex can give physicians more objective results in diagnosis. In this study, sleep EEG records scored by a physician were analyzed in different methods from the literature. Three features have been determined that express the k-complex presence and k-complexes were detected using these features and support vector machines. As a result, the performance of the algorithm was evaluated and sensitivity and specificity were determined as 70.83% and 85.29%, respectively.
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
Development of Decision Support System for Automatic Sleep Stage Scoring
(2018) Gulhan, Gizem; Balcik, Irem; Kantar, Tugce; Erdamar, Aykut
In the diagnosis of sleep disorders, analyzing the patient's sleep stage scores is the basic clinical procedure. The signals obtained from the polysomnography device are used in the sleep stage scoring studies. The polysomnography device records the physiological signals from different channels through electrodes during patients' night sleep. Aim of the study is developing a decision support system which determines the state of sleep and wakefullness by using only one channel electroencephalography signal. The amplitude and frequency values of the sub-bands of the electroencephalogram signal change in the sleep stages. Accordingly, five attributes were determined and classified using support vector machines As a result of the classification study, accuracy, sensitivity and specificity values were calculated for the performance evaluation of the algorithm.
An efficient automatic arousals detection algorithm in single channel EEG
(2019) Ugur, Tugce Kantar; Erdamar, Aykut; 0000-0001-8588-480X; 31046987; AAA-6844-2019
Background 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.
Image Analysis For Single Cell Gel Electrophoresis
(2017) Mese, Alev Kakac; Erdamar, Aykut; Iseri, Ozlem Darcansoy; 0000-0001-8588-480X; AAA-6844-2019
Detection of dioxyribonucleic acid (DNA) damage is very crucial in various areas of life sciences and in the clinical diagnosis of some pathophysiologies. Single cell gel electrophoresis, also called Comet Assay, is a reliable and easily applicable method to measure/detect level of DNA damage which is an indicator of an genotoxic and cytotoxic effect on living organisms caused by chemical and phsyical activity. The method is generally based on the fact that the DNA in the nucleus isolated from living tissues is placed in a thin agarose gel and run on an electrophoretic medium. DNA images obtained with the Comet protocol can be evaluated visually as well as can be analyzed using various software today. With such software, objective results can be obtained in a short period of time and without adhering to the researcher's experience. In this study, Comet analysis images obtained from HepG2 (ATCC HB-8065) hepatocellular carcinoma liver cancer cells were used. Calculation of measurement results of these user-selected images and presenting parametric data to the user are intended.
Multi-scale classification of single-cell gel electrophoresis assay using deep learning algorithm
(2020) Erdamar, Aykut; Aksahin, Mehmet Feyzi; 0000-0001-8588-480X; AAA-6844-2019
Structural 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.
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-7861-2018
Image 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.
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.
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-6844-2019
Objective: 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.
Quantitative sleep EEG synchronization analysis for automatic arousals detection
(2020) Erdamar, Aykut; Aksahin, Mehmet Feyzi; 0000-0001-8588-480X; AAA-6844-2019
Background 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.
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.
Sleep Apnea Detection Using With EEG, ECG and Respiratory Signals
(2017) Aksahin, Mehmet Feyzi; Erdamar, Aykut; Isik, Atakan; Karaduman, Asena; 0000-0001-8588-480X; AAA-6844-2019
Sleep apnea is one of the major sleep disorders of today. The diagnosis of sleep apnea is performed by specialist physicians. This siruation extends the duration of the diagnosis. To shorten this period and at the same time to avoid the mistakes that may occur in diagnosis, an automated decision support system has been considered in the diagnosis and classification of sleep apnea. In this study, ECG signal was analyzed to obtain Heart Rate Variability (HRV) signal and the power spectral density (PSD) of this signal was examined. It has been observed that the low and high frequency energy ratios are different in the PSD of examined HRV. Parallel to this analysis, the energy of the respiratory signal is obtained and it is understood that there is a significant energy exchange in the apnea cases. However, the powers of the frequency bands in the EEG signal were found separately and the ratios of these bands to each other were calculated. In the analysis, it was observed that the ratios of and non-apnea periods. By using these differences, an artificial neural network (ANN) algorithm is constructed to diagnose and classify the sleep apnea. This algorithm was tested on two patient data; ANN was trained and tested separately for each patient. As a result, it was determined that the average accuracy rate of ANN is high.
Tek hücre jel elektroforezi için görüntü analizi
(Başkent Üniversitesi Fen Bilimleri Enstitüsü, 2017) Kakaç Meşe, Alev; Erdamar, Aykut
Genetik bilginin nesilden nesile sağlıklı olarak aktarılabilmesi için deoksiribonükleik asit (DNA) yapısının korunması son derece önemlidir. Ancak, hücrenin normal metabolik süreçlerinde ya da fiziksel veya kimyasal etkilerle DNA’da hasar oluşabilmektedir. Yaşam bilimlerinin çeşitli alanlarında ve bazı patofizyolojilerin klinik tanısında DNA hasarının tespiti oldukça önemlidir. Tek hücre jel elektroforezi (THJE) veya diğer adıyla Comet analizi fiziksel ve kimyasal etkilerin canlılar üzerinde yol açtığı genotoksik ve sitotoksik etkilerin bir göstergesi olan DNA hasar seviyelerinin ölçülmesinde kullanılan güvenilir ve kolay uygulanabilir bir yöntemdir. Yöntem genel olarak, canlı dokulardan izole edilen çekirdek içindeki DNA’nın, ince bir agaroz jel içine yerleştirilip elektroforetik ortamda yürütülmesini temel alır. Comet protokolü ile elde edilen DNA görüntüleri gözle değerlendirilebileceği gibi günümüzde çeşitli yazılımlar kullanılarak da analiz edilmektedir. Bu tür yazılımlar sayesinde kısa sürede ve araştırmacının deneyimine bağlı kalınmayarak objektif sonuçlar elde edilebilmektedir. Literatürde bulunan mevcut çalışmalar, Comet analiz sistemlerinin yarı otomatik ve otomatik olarak ikiye ayrıldığını göstermektedir. Yarı otomatik yazılımlar çoğunlukla kullanıcıdan bir eşik değeri, DNA’yı seçme ve Comet görüntüsünün baş kısmını seçmelerini ister. Otomatik yazılımlar ise kullanıcı tarafından seçilen veya görüntü boyunca yer alan Comet analiz görüntülerini otomatik olarak analiz edip ölçüm sonuçlarını sunar. Bu çalışmada, HepG2 (ATCC HB-8065) hepatosellüler karsinom karaciğer kanser hücrelerinden elde edilen Comet analiz görüntüleri kullanılmıştır. Kullanıcı tarafından seçilen bu görüntülerde, ölçüm sonuçlarının hesaplanması ve kullanıcıya parametrik veriler sunulması amaçlanmıştır. Preservation of the structure of dioxyribonucleic acid (DNA) is crucial so that genetic information can be transmitted in a healthy way from generation to generation. However, DNA could be damaged by metabolic processes of a cell or by chemical or phsycial activity. Detection of DNA damage is very crucial in various areas of life sciences and in the clinical diagnosis of some pathophysiologies. Single cell gel electrophoresis, also called Comet Assay, is a reliable and easily applicable method to measure/detect level of DNA damage which is an indicator of an genotoxic and cytotoxic effect on living organisms caused by chemical and phsyical activity. The method is generally based on the fact that the DNA in the nucleus isolated from living tissues is placed in a thin agarose gel and run on an electrophoretic medium. DNA images obtained with the Comet protocol can be evaluated visually as well as can be analyzed using various software today. With such software, objective results can be obtained in a short period of time and without adhering to the researcher's experience. Current studies in the literature show that Comet analysis systems are categorized into two as semi-automatic and automatically. Semi-automated software often requires the user to select a threshold value, the DNA, and the head of the Comet image. The automatic software automatically analyzes the Comet analysis images selected by the user or located throughout the image and presents the measurement results. In this study, Comet analysis images obtained from HepG2 (ATCC HB-8065) hepatocellular carcinoma liver cancer cells were used. Calculation of measurement results of these user-selected images and presenting parametric data to the user are intended.
Uyku bozukluklarına ait eeg verilerindeki geçici eeg dalga formlarının analizi
(Başkent Üniversitesi Fen Bilimleri Enstitüsü, 2017) Kantar, Tuğçe; Erdamar, Aykut
Uykuda anlık olarak ortaya çıkan, kendine özgü yapısal özellikleri, genlik ve frekansları olan, elektroensefalografi (EEG)’nin arka planından ayırt edilmesi zor geçici dalga formları(k-kompleksler, uyku iğcikleri, arousal vb.) EEG’nin mikro yapısı olarak adlandırılır. Bu dalga formlarının analizi beyin araştırmaları, uyku çalışmaları, uyku evre skorlamaları, uyku bozukluklarının değerlendirilmesi açısından önemlidir. Literatürde bulunan mevcut çalışmalar, geçici dalga formlarının belirlenen öznitelikleri kullanılarak yapılan tespit ve sınıflandırma çalışmaları şeklindedir. Bu tez çalışmasında, literatürdeki çalışmalardan farklı olarak, tek bir geçici EEG dalga formu için değil, üç farklı dalga formu için yüksek doğruluk oranında tespit yapacak yöntemler geliştirilmesi amaçlanmıştır. Çalışmada, National Sleep Research Resource, DREAMS veri tabanları ve Ankara Gülhane Askeri Tıp Akademisi Ruh Sağlığı ve Hastalıkları Anabilim Dalı uyku laboratuvarında yapılan uyku skorlandırma çalışmaları sırasında kaydedilen gerçek hastalara ait polisomnografi kayıtları kullanılmıştır. Çalışmanın ilk aşamasında, sinyal işleme yöntemleri kullanılarak veri tabanı ve hasta kayıtlarındaki EEG sinyallerinin analizleri gerçekleştirilmiştir. Spektral analizlerde, ayrık Fourier dönüşümü, güç spektrumu yöntemleri, zaman-frekans analizinde kısa zamanlı Fourier dönüşümü yöntemi, dalgacık analizlerinde ayrık dalgacık dönüşümü ve sürekli dalgacık dönüşümü yöntemleri kullanılmıştır. Dalga formu analizlerinde ise, EEG’nin etkin enerji değeri, alt bant enerji analizi, çapraz ilinti fonksiyonu, sıfır kesme oranı, ortalama ve varyans gibi sinyal parametreleri incelenmiştir. Uygulanan analizler sonucu uykudaki geçici dalga formlarından k-kompleks için normalize çapraz ilinti fonksiyonu, alt bant enerji analiz değerleri ve sıfır kesme oranı; uyku iğcikleri için sürekli dalgacık dönüşümü sonucu uyku iğciğinin sahip olduğu karakteristik frekans aralığındaki dalgacık dönüşümükatsayılarının toplamı; arousal için ise sürekli dalgacık dönüşümü sonucu elde edilen aralıkta sinyalin ortalama ve varyans değerleri öznitelik olarak belirlenmiştir. Çalışmanın ikinci aşamasında, doğrusal ayırtaç analizi, destek vektör makineleri ve yapay sinir ağları kullanılarak belirlenen öznitelikler ile geçici dalga formu “var” ya da “yok” şeklinde sınıflandırma yapılmıştır. Çalışma sonucunda, literatürden daha iyi veya literatürle eş seviyede sonuçlar veren ve başarılı olarak tespit yapabilen bir karar destek sistem algoritması geliştirilmiştir. Transient waveforms (k-complexes, sleep spindles, arousal, etc.) happens instantaneously in sleep, have distinctive structural features, amplitudes and frequencies, and are difficult to distinguish from the background of electroencephalography (EEG) which are called the microstructure of the EEG. The analysis of these waveforms is important for brain research, sleep studies, sleep stage scorings and assessment of sleep disorders. In literature, related works are used the specified feature extraction of the transient waveforms and classification studies. In this study, different from the literature, it is aimed to develop several methods to detect high accuracy three different waveforms with high accuracy, unlike single transient EEG waveform. In this study, the EEG recordings were obtained from the National Sleep Research Resource, DREAMS databases and Ankara Gulhane Military Medical Academy, Psychiatry Department the Sleep Laboratory. In the first part of the study, signal processing methods were used to analyze patients’ EEG signal records. Discrete Fourier transform, power spectrum methods in spectral analysis; short - time Fourier transform in time-frequency analysis; discrete and continuous wavelet transform in the wavelet analysis were used. In wave form analyzes, signal parameters such as effective value of EEG energy, sub-band energy analysis, cross correlation function, zero cross rate, mean and variance were investigated. According to results, cross correlation function, sub-band energy analysis and zero cross rate for k-complex; the sum of the continuous wavelet transform coefficients in the frequency range for sleep spindles; and the mean and variance of the continuous wavelet transform coefficients for arousal were determined as features. In the second part of the study, with determined features, transient waveforms were classified as "existence" or "absence" using linear discriminant analysis, support vector machines and artificial neural networks. As a result of the study, a decision support system algorithm, can detect transient waveforms successfully, was developed that can yield results better than or as equivalent as the literature.