Development of hybrid artificial intelligence based automatic sleep/awake detection

Abstract

Background and Objective: Obstructive Sleep Apnea is a disease that causes respiratory arrest in sleep and reduces sleep quality. The diagnosis of the disease is made by the physician in two stages by examining the patient records taken with the polysomnography device. Because of the negative aspects of this process, new diagnostic processes and devices are needed. In this article, a new approach to sleep staging, which is one of the diagnostic steps of the disease, was proposed. An artificial intelligence-based sleep/awake system detection was developed for sleep staging processing. Photoplethysmography (PPG) signal and heart rate variable (HRV) were used in the study. PPG records taken from patient and control groups were cleaned by the digital filter. The HRV parameter was then derived from the PPG signal. Then, 40 features from HRV signal and 46 features from PPG signal were extracted. The extracted features were classified by reduced machine learning techniques with F-score feature selection method. In order to evaluate the performances of the classifiers, the sensitivity and specificity values, the accuracy rates for each class were computed in the test set and receiver operating characteristic curve prepared. In addition, area under the curve (AUC), Kappa coefficient and F-score were calculated. According to the results obtained, the system can be realised with 91.09% accuracy rate using 11 PPG and HRV and with 90.01% accuracy rate using 14 HRV features. These success rates are quite enough for the system to work. When all these values are taken into consideration, it is possible to realise a practical sleep/awake detection system. This article suggests that the PPG signal can be used to diagnose obstructive sleep apnea by processing with artificial intelligence and signal processing techniques.