Enhancing Obstructive Sleep Apnea Diagnosis: Integrating Automated Detection and Event Duration Analysis

Obstructive sleep apnea (OSA) is a sleep disorder marked by repeated episodes of airway obstruction, leading to apneas (complete blockage) or hypopneas (partial blockage) during sleep. The standard diagnostic metric, the apnea-hypopnea index (AHI), quantifies the number of these events per hour of sleep but has limitations, such as its dependence on manual interpretation and lack of attention to event duration, which can be clinically significant. To address these issues, this study developed an algorithm to detect respiratory events from nasal airflow signals and measure their duration, using data from 22 patients at St. Vincent's University Hospital, sourced from the PhysioNet dataset. Signal processing techniques, including filtering and envelope analysis, were applied to extract features, and apnea/hypopnea events were identified based on American Academy of Sleep Medicine (AASM) guidelines. Events were classified by duration into three groups: 10-20 seconds, 20-40 seconds, and over 40 seconds. Preliminary results showed detection accuracy of 60% for apnea and 93% for hypopnea events. The study also explored relations between event duration and demographic factors, such as age, gender, body mass index (BMI), and Epworth Sleepiness Scale (ESS) scores, to assess whether longer events were linked to greater severity. These findings suggest that incorporating event duration and automated detection into OSA diagnosis could improve accuracy and provide better insight into the condition, potentially leading to more personalized treatments.