Fakülteler / Faculties
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Item Sleep Apnea Detection Using Blood Pressure Signal(2018) Aksahin, Mehmet Feyzi; Oltu, Burcu; Karaca, Busra KubraSleep apnea is a common respiratory disease. Apnea affects sleep quality, reduces people's life standards, and it can result in death at advanced stage. Therefore the ability to detect the apnea quickly and accurately is important for the treatment of this disease. Apnea is diagnosed by specialists however this is a long and exhausting process. Accordingly, a decision support system that automatically diagnoses apnea has been developed to facilitate this process and make it more objective. The developed decision support system in this study is based on patient's blood pressure signals instead of traditional Polysomnography (PSG) records, which requires various physiological signals measured from the patients. In the examined blood pressure signals, the change that results from each heart beat was determined and heart rate variability (HRV) was calculated based on these changes. At the same time, maximum and minimum amplitude values were found for each change period and amplitude variability vector was created. The features for each epoch were determined using the generated amplitude variability vector and HRV data. Presence of apnea in each epoch is classified with determined features and with the use of "Quadratic SVM" classifier. The Quadratic SVM classifier was trained with 87.5% accuracy and then the system is tested. As a result 75.4% sensitivity and 75% positive predictive values were obtained.Item 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, AykutCardiovascular 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.Item Classification of Sleep Apnea by Photoplethysmography Signal(2018) Aksahin, Mehmet Feyzi; Karaca, Busra Kubra; Oltu, BurcuSleep apnea is a very common respiratory disorder in the community that includes a range from upper airway obstruction to respiratory abnormalities and the absence of a breathing effort, which can lower people's standard of living and even cause death. Therefore, the sleep apnea needs to be diagnosed in a practical way and with high accuracy. The diagnosis of apnea is made by recording the physiological parameters of the patient with polysomnography (PSG) method and by examination of these parameters by specialist physicians, but it is a tedious and time consuming process. In order to simplify the apnea diagnosis process, phospletismography (PPG) signals are used instead of PSG records. PPG signals are suitable for diagnosis of apnea because they reflect changes in respiration. In the proposed study, a decision support system was developed to automatically diagnose apnea and to make apnea diagnosis easier and more objective using PPG signals. In the decision support system, the peaks of the PPG signal were determined and the heart rate variability (HRV) vector was generated depending on the time difference between these peaks. The mean and standard deviation values of the generated vector are determined as features for each epoch. The presence of the apnea at each epoch is classified using "Subspace K Nearest Neighbor (Subspace KNN)" and specified features. The "Subspace KNN" classifier was trained with 85% accuracy and then system was tested. As a result, sensitivity, accuracy and specificity rates were calculated as 91%, 95% and 90% respectively.Item Heart sound recording and automatic S1-S2 waves detecting system design(2020) Aksahin, Mehmet Feyzi; Oltu, Burcu; Karaca, Busra KubraThe second leading cause of death in the world is cardiovascular diseases. Diagnosis of vast majority of cardiovascular diseases is made by listening to heart sounds by specialists (auscultation method). However, since the method of auscultation depends on the experience and hearing ability of the specialist, obtained results can be subjective. Therefore, digitization and visualization of heart sounds enables accurate, rapid and economical diagnosis of cardiovascular diseases, especially heart valve diseases. For this purpose, a device prototype that collects the heart sound from human body and also amplifies, filters, displays and records collected data on digital environment was designed in the first part of this study. In order to test the working accuracy of the designed device, clinical applications were carried out with the permission of the ethics committee and as the result of this application 15 heart sound recordings from 5 different disease groups(mitral insufficiency, mitral-aortic insufficiency, mitral-tricuspid insufficiency, mitral-aortic tricuspid insufficiency and healthy heart sound recordings) were collected.and obtained recordings were examined. The most effective parameter for the diagnosis of heart valve diseases is the location of the S1-S2 heart sounds. For this reason, in the second part of the study, a medical decision support system was established to detect the S1-S2 locations to assist physicians in their diagnosis. In this context, heart sounds are first filtered by discrete wavelet transform. Then, the S1-S2 waves in the filtered signal are made evident by the teager energy operator and rule-based algorithm. As a result, S1-S2 locations in normal and pathological data were detected with 98.67% sensitivity, 97.69% specificity and 98.18% accuracy.