Time domain characterization for sleep apnea in oronasal airflow signal: a dynamic threshold classification approach

Apneas are the most common type of sleep-related breathing disorders; they cause patients to move from restorative sleep into inefficient sleep. The American Academy of Sleep Medicine (AASM) considers sleep apnea as a hidden health crisis that affects 29.4 million adults, costing the USA billions of dollars. Traditional detection methods of sleep apnea are achieved by human observation of the respiration signals. This introduces limitations in terms of access and efficiency of diagnostic sleep studies. However, alternative device technologies have limited diagnostic accuracy for detecting apnea events although many of the previous investigational algorithms are based on multiple physiological channel inputs. Guided by the AASM recommendations for sleep apnea diagnostics, this paper investigates time domain metrics to characterize changes in oronasal airflow respiration signals during the occurrence of apneic events.

A new algorithm is developed to derive a respiratory baseline from the oronasal airflow signal in order to detect sleep apnea events using a dynamically adjusted threshold classification approach. To demonstrate our results, we use polysomnography data of [Formula: see text] patients with different apnea severity levels as reflected by their overnight apnea hypopnea index (AHI), including patients with mild apnea (5 [Formula: see text] AHI [Formula: see text]), moderate apnea ([Formula: see text] AHI [Formula: see text]), and severe apnea (AHI [Formula: see text]).

Our results indicate the ability to characterize sleep apnea events in oronasal airflow signals using the proposed dynamic threshold classification approach. Overall, the new algorithm achieved a sensitivity of 80.0%, specificity of 88.7%, and an area under receiver operating characteristics curve of 0.844.

The present results contribute a new approach for progressive detection of sleep apnea using an adaptive threshold that is dynamically adjusted with respect to the patient's respiration baseline, making it potentially able to effectively generalize over patients with different apnea severity levels and longer monitoring periods.