Sleep apnea syndrome (SAS) is a common and serious sleep disorder. Apnea is a cessation of airflow during sleep which lasts for at least 10 seconds with at least 3% drop in the level of the oxygen saturation in the blood. The other breathing disorder during sleep is called hypopnea which is defined as 50% decrease in airflow for at least 10 seconds and a minimum of 3% drop in the oxygen saturation level of blood. Apnea/Hypopnea Index (AHI) is defined as the number of apnea/hypopnea occurrences during an hour which indicates the severity of the SAS. Sleep apnea complications and risks include excessive daytime sleepiness, lack of concentration, sleep deprivation, increased blood pressure, metabolic syndromes (glucose intolerance) and risk of cardiac morbidity. Importantly, sleep apnea is highly prevalent in the general population, approaching 5-10% (~21 M) of the US adult population, but most cases are thought to go undiagnosed. Also, it is found in up to 70% of stroke victims, 60% of congestive heart failure patients, and 70% of obese patients.

Polysomnography (PSG) during the entire night is currently the gold standard diagnostic method of sleep apnea. The standard PSG consists of recording more than 15 physiological signals including EEG, ECG, EMG of chins and legs, nasal airflow, electro-oculogram (EOG), abdominal and thoracic movements, and blood oxygen saturation (SaO2). However, the high cost of the system, discomfort of the electrodes connecting to the body and the high amount of information required to be analyzed are the main disadvantages of this method. Right now, to obtain a full sleep study in Manitoba there is currently a long waiting list (3300 patients) and long waiting times (3.4 to 8.3 years). Hence, looking for alternative methods, portable devices, and automated/intelligent systems in which sleep apnea testing can be done faster and/or in the patient’s home is of great interest.

The goal of my study is to develop an acoustical method for monitoring and detection of SAS based on tracheal sound signal and oxygen saturation level in the blood. First an automated algorithm finds the artifacts of tracheal sounds (that normally appear as impulses in the signal) and removes them from further analysis. Then, a smart algorithm uses oxygen saturation information and different features of tracheal sounds to detect apnea/hypopnea episodes. Finally the duration and frequency of occurrences of apnea/ hypopnea events during the entire sleep are presented as a diagnostic aid to the physician.

The smart program has been developed and it is going through the validation process. After debugging the codes and satisfying the validation requirements, the research could be used to develop a prototype of an integrated system to acquire, denoise, analyze the tracheal respiratory sounds, estimate airflow acoustically, detect apnea episodes, report the duration and frequency of apnea, and to use wireless technology to transfer data to a remote clinical diagnostic center. Such a system will reduce the need for PSG test, hence reducing the long waiting list for an accurate diagnostic assessment. The system proposed in this study also facilitates studying patients with mobility or behavioral cognitive issues.

Long distance monitoring and diagnostic aid tools provide large financial saving to both the health care system and families. This study will provide a novel system to both developing a new and yet simple diagnostic tool for sleep apnea disorder, and also a new way to connect the specialists and physicians with patients either in remote areas or even at their homes. From a public health perspective, non-invasive and inexpensive methods to determine airway responses across all ages and conditions would present a major step forward in the management of sleep apnea disorders; hence, a significant benefit to Canada.