Cross-Frequency EEG Analysis for OSA Severity and Sleepiness

The patent extracts and focuses on only the most critical information from the full polysomnography dataset - specifically EEG signals at C3 and C4 channels that correlate with sensorimotor network activity and sleepiness. By taking out only these essential components rather than using all 18 sensors, the system achieves sufficient measurement precision for diagnosing OSA severity and assessing daytime sleepiness while dramatically reducing device complexity

The patent creates a simplified copy of the comprehensive diagnostic system that uses only two EEG channels instead of 18 sensors. This copying approach maintains the core diagnostic capability for OSA severity classification and sleepiness assessment while using a fraction of the original system's complexity and cost

The patent implements self-service through automated machine learning algorithms that perform quality assurance and diagnostic evaluation without requiring expert human review. The system automatically processes the simplified EEG data, classifies OSA severity, and assesses sleepiness using trained models, eliminating the time-consuming manual evaluation step while maintaining diagnostic accuracy

The patent replaces the mechanical process of human expert evaluation with automated computational algorithms. Instead of having experts visually inspect and manually score the polysomnography data, the system uses machine learning models to automatically detect patterns, classify severity levels, and generate diagnostic reports, thereby reducing time loss while maintaining precision