@batman.edu.tr
Facult of Engineering/Electrical-electronics Engineering
Batman University
Bachelor Degree: Dicle University
Master Degree: Sakarya University
PhD Degree: Dicle University
Scholar Citations
Scholar h-index
Scholar i10-index
1- Evaluation of the relationship between Chronic Obstructive Pulmonary Disease and photoplethysmography signal
Chronic Obstructive Pulmonary Disease is a common respiratory disease. The diagnosis of the disease is made by a specialist doctor according to the report given by the spirometer device, which is the gold standard method for diagnosis. This method can only be performed in hospitals with the help of a technician. However, monitoring of the disease after diagnosis is important to follow the damage that the disease inflicts on the body. That's why the portability of diagnostic systems is vital. As a solution to these problems, there is a need for a portable system alternative to the spirometer device. In the study, a photoplethysmographic signal was suggested for the diagnosis of the disease. In the study, feature extraction was performed on 26 time domain from the photoplethysmographic signal and these properties were analyzed by Mann-Whitney U Test. According to the results obtained, p <; 0.05 for features 2-6, 10 …
2- A new approach for treatment of chronic obstructive pulmonary disease
Chronic Obstructive Pulmonary Disease (COPD) is a persistent respiratory disease usually caused by toxic gases. The diagnosis is made by a specialist doctor on a report taken by a specialist technician using a spirometer. Diagnostic steps can only be carried out in hospital environment in the presence of a qualified technician. The diagnostic process is so troublesome that it leads to alternative system requirements. In this study, a portable software system based on photoplethysmography signal is proposed as an alternative method to reduce the burden of the diagnosis process of the disease. For this purpose, 26 features were extracted from the photoplethysmography signal in time domain. The extracted features were classified by machine learning based k - Nearest Neighbors algorithm and tried to diagnose the disease. The study included 8 patients with COPD and a control group of 6 patients. Parameters …