Investigation of Methods to Detect Respiratory Irregularities in Obstructive Sleep Apnea (OSA)

Obstructive Sleep Apnoea (OSA) is a common sleep-breathing disorder affecting around 2-4% of the population and is characterised by loud snoring, periodic collapse of the upper airway particular to sleep, sleep fragmentation, and sometimes daytime sleepiness. Health consequences include impaired cognitive functioning, quality of life, mood, and increased cardiovascular disease risk. Epidemiological studies have demonstrated that increased morbidity and mortality in a dose response manner with increasing severity of sleep disordered breathing.

The standard treatment for OSA is nasal Continuous Positive Airway Pressure (CPAP), which consists of pressurized air applied to the nose via a mask. Generally a single, optimal pressure is prescribed for a patient based on a full night or partial night study during which the pressure is adjusted by a technician until all sleep disordered breathing (SDB) events are abolished. Despite therapy efficacy, compliance to CPAP therapy is sub-optimal.

Due to low compliance it has become common for healthcare providers to follow up subject therapy efficacy through reporting software inherent in many current CPAP devices. Healthcare providers can use this reported data to make appropriate treatment decisions. For this reason the data needs to be accurate. The SleepStyle™ 200 Auto Series CPAP humidifier is an auto adjusting CPAP which stores comprehensive compliance data. The aim of this study is to compare the reliability of the SleepStyle™ 200 Auto Series CPAP data to laboratory-based PSG in detecting sleep disordered breathing.

Hypothesis: The SleepStyle™ 200 Auto Series reporting features accurately detects indices of sleep disordered breathing.