HHT based cardiopulmonary coupling analysis for sleep apnea detection.

Area of Science:

• Cardiorespiratory Physiology
• Signal Processing
• Sleep Medicine

Background:

• Cardiopulmonary coupling (CPC) analysis reveals interactions between cardiac and respiratory systems.
• Traditional CPC methods lack sufficient time-frequency resolution for detailed sleep apnea analysis.
• The Hilbert-Huang Transform (HHT) offers advanced signal decomposition capabilities.

Purpose of the Study:

• To validate the feasibility of HHT-based CPC for respiratory event detection.
• To assess the HHT-CPC technique's ability to estimate the severity of sleep apnea/hypopnea.
• To compare the performance of HHT-CPC with conventional CPC methods.

Main Methods:

• Applied HHT-CPC sleep spectrogram technique to 69 ECG signals from the Physionet Sleep Apnea Database.
• Compared HHT-CPC spectrograms with original CPC for structure and time-frequency resolution.
• Evaluated respiratory event detection using power of low frequency coupling (pLFC) via ROC analysis.
• Computed correlation between HHT-CPC index (TVDF) and Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) scoring.

Main Results:

• HHT-CPC spectrum achieved significantly finer temporal (8 s) and frequency (0.001 Hz) resolution than original CPC.
• pLFC demonstrated an area under the ROC curve of 0.79 for distinguishing respiratory events.
• TVDF showed significant differences across OSAHS severity groups (p<0.001).
• TVDF exhibited a strong negative correlation with the apnea/hypopnea index (AHI, r=-0.71).

Conclusions:

• HHT-CPC spectrum provides detailed temporal-frequency information on cardiopulmonary coupling during sleep.
• pLFC and TVDF serve as spectrographic markers for identifying respiratory events and sleep architecture disruption in sleep apnea.
• The HHT-CPC technique may enhance diagnostic capabilities for sleep-related breathing disorders.