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Author: search.filters.author.Aksahin, Mehmet FeyziSubject: search.filters.subject.sleep apnea

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    Detection of Hypopnea Using Respiratory Signals
    (2019) Oktan, Aynur Didem; Aksahin, Mehmet Feyzi
    Hypopnea is a respiratory disorder that affects people's sleep quality and reduces their standard of living. Detection and treatment of sleep disorders are costly. It requires time and effort. Because patients have to spend their time with special systems in which their physiological signals are recorded and specialist personnel in their sleep laboratories. Polysomnograms should be analyzed by medical doctors every night. Reliable sleep stage scoring is done manually by experts. This means that each morning, a specialist visually analyzes the 960 period of an eight-hour polysomnogram to create a hypnogram. This requires a long time. In this study, a method for automatic detection of hypopnea by eliminating the effect of the doctor is proposed. In this method, epoxes were scored by using air flow, thorax and abdominal amplitude information obtained from the person. A training data was created using hypopnea and normal epochs and grading was performed using the determined attributes. Quadratic Support Vector Machines (SVM) gave the highest accuracy when determining the presence of hypopnea. The linear DVM method was trained in 90.6% accuracy and the system was then tested. It was found that hioped epochs can be detected with 90% sensitivity.
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    Sleep Apnea Detection Using Blood Pressure Signal
    (2018) Aksahin, Mehmet Feyzi; Oltu, Burcu; Karaca, Busra Kubra
    Sleep 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.
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    Classification of Sleep Apnea by Photoplethysmography Signal
    (2018) Aksahin, Mehmet Feyzi; Karaca, Busra Kubra; Oltu, Burcu
    Sleep 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.