Browsing by Author "Kantar, Tugce"

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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 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.
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.