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Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals.
1Department of Electronics and Communication Engineering, Agni College of Technology, Chennai, Tamil Nadu, India.
Scientific Reports
|March 6, 2024
Summary
This study used electroencephalogram (EEG) brain connectivity to classify obstructive sleep apnea (OSA). Delta band EEG analysis revealed distinct positive correlations in OSA patients, differentiating them from healthy individuals.
Area of Science:
- Neuroscience
- Medical Engineering
- Signal Processing
Background:
- Obstructive sleep apnea (OSA) is a serious condition causing upper airway blockage during sleep.
- Severe OSA increases risks of heart attack, stroke, and mortality.
- Accurate classification of OSA is crucial for timely intervention and management.
Purpose of the Study:
- To explore the classification of obstructive sleep apnea (OSA) and healthy subjects.
- To investigate brain connectivity patterns using electroencephalogram (EEG) signals for OSA detection.
- To identify specific EEG bands and connectivity metrics indicative of OSA.
Main Methods:
- Utilized the ISRUC database with 50 four-channel EEG signals.
- Applied a 50 Hz notch filter for noise removal.
- Employed Wavelet Packet Decomposition to segregate EEG signals into Gamma, Beta, Alpha, Theta, and Delta bands.
- Performed brain connectivity analysis using 4 electrode positions and Pearson correlation.
Main Results:
- The delta EEG band showed the highest positive correlation in OSA subjects (0.7331–0.9172) compared to healthy subjects (0.6995).
- Delta band connectivity demonstrated a stronger association with brain activity in OSA patients.
- Distinct positive correlation patterns in the delta band effectively classified OSA from healthy individuals.
Conclusions:
- Brain connectivity analysis of EEG signals, particularly in the delta band, can effectively classify obstructive sleep apnea.
- The delta band's correlated activity provides a potential biomarker for OSA detection.
- This method offers a non-invasive approach for OSA diagnosis and characterization.
