Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis

Area of Science:

• Cardiology
• Sleep Medicine
• Biomedical Engineering

Background:

• Cardiopulmonary coupling (CPC) analysis is an electrocardiogram (ECG)-based technique.
• The application of CPC analysis in underserved populations for detecting obstructive sleep apnea (OSA) is underexplored.
• Polysomnography (PSG) is the current gold standard for OSA diagnosis.

Purpose of the Study:

• To evaluate the reliability of CPC analysis for detecting OSA.
• To compare CPC analysis outcomes with PSG-derived sleep outcomes.
• To explore the potential of ECG-based CPC analysis for OSA diagnosis.

Main Methods:

• Two hundred five PSG datasets were analyzed.
• Automated CPC analysis was performed using only ECG signals.
• Agreement between CPC-derived Respiratory Event Index (REI) and PSG-derived Apnea-Hypopnea Index (AHI) was tested.

Main Results:

• CPC-REI showed a strong positive correlation with PSG-AHI (r=0.851, p<0.001).
• This correlation remained significant after adjusting for age and gender (r=0.840, p<0.001).
• CPC analysis demonstrated good sensitivity and specificity for OSA detection.

Conclusions:

• The CPC approach provides acceptable results for OSA patients when compared to PSG.
• ECG recordings utilizing CPC analysis can be effectively used for OSA screening and diagnosis.
• This technique holds promise for broader application in general population screening.