A deep learning-based decision support system for diagnosis of OSAS using PTT signals

Abstract

Sleep disorders, which negatively affect an individual's daily quality of life, are a common problem for most of society. The most dangerous sleep disorder is obstructive sleep apnea syndrome (OSAS), which manifests itself during sleep and can cause the sudden death of patients. Many important parameters related to the diagnosis and treatments of such sleep disorders are simultaneously examined. This process is exhausting and time-consuming for experts and also requires experience; thus, it can cause difference of opinion among experts. Because of this, automatic sleep staging systems have been designed. In this study, a decision support system was developed to determine OSAS patients. In the developed decision support system, unlike in the available published literature, patient and healthy individual classification was performed using only the Pulse Transition Time (PTT) parameter rather than other parameters obtained from polysomnographic data like ECG (Electrocardiogram), EEG (Electroencephalography), carbon dioxide measurement and EMG (Electromyography). The suggested method can perform feature extraction from PIT signals by means of a deep-learning method. AlexNet and VGG-16, which are two Convolutional Neural Network (CNN) models, have been used for feature extraction. With the features obtained, patients and healthy individuals were classified by the Support Vector Machine (SVM) and the k-nearest neighbors (k-NN) algorithms. When the performance of the study was compared with other studies in published literature, it was seen that satisfactory results were obtained.