A Master of Science thesis in Computer Engineering by Mamoun Al-Mardini entitled, "A Framework for Screening and Classifying Obstructive Sleep Apnea Using Smartphones," submitted in June 2013. Thesis advisor is Dr. Fadi Aloul and Co-advisor is Dr. Assim Sagahyroon. Available are both soft and hard copies of the thesis.
Obstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the golden standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In the proposed research, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone's built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory efforts, and (3) an accelerometer to detect the body's movements. The collected signals are then analyzed on the phone to deduce if the patient is suffering from OSA. In the proposed system, we have developed an Android application that is able to record and extract the physiological signals from the patients and analyze them solely on the smartphone without the need for any external resources. The smartphone is able to analyze the oximeter and accelerometer reading. Most health applications use smartphones to collect physiological readings, and then off load them to an external server for analysis. However, in this work we developed an integrated environment that collects and processes data on the smartphone, including the signal processing functions that analyze the recorded respiratory efforts. Finally, we examine our system's ability to screen the disease when compared to the golden standard by testing it on 17 samples. The results showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. These preliminary results demonstrate the effectiveness of the proposed system as compared to the golden standard and emphasize the important role of smartphones in healthcare.