Awake EEG Sleep Apnea Detection Using Brain Electrodynamics
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Solution Overview
Problem
Diagnosing pediatric obstructive sleep apnea (OSA) is challenging due to the invasiveness and expense of overnight polysomnograms, and alternative methods lack diagnostic accuracy.
Innovation Solution
A computer-based system transforms awake EEG data into features using topological data analysis (TDA) and recurrence quantification analysis, applying machine learning models to detect sleep apnea by analyzing brain electrodynamics via EEG electrodes, eliminating the need for overnight studies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If overnight polysomnograms are used to diagnose pediatric obstructive sleep apnea, then diagnostic accuracy is improved, but the procedure becomes invasive and expensive
Solution Approach 1:
The patent extracts the essential diagnostic function from the complex overnight polysomnogram procedure by using only awake EEG data combined with machine learning algorithms. This extracts the core diagnostic capability while removing the invasive overnight monitoring requirement, achieving accurate OSA detection without the harmful invasiveness of traditional methods
Solution Approach 2:
The patent creates a computational model that copies the diagnostic patterns learned from training data (including polysomnogram results) and applies this copied knowledge to new patients through machine learning. This allows the system to replicate expert diagnostic accuracy without requiring actual overnight studies, eliminating invasiveness while preserving measurement precision
2Measurement precision
If overnight polysomnograms are used to diagnose pediatric obstructive sleep apnea, then diagnostic accuracy is improved, but the cost increases
Solution Approach 1:
The patent replaces the expensive overnight polysomnogram procedure with a low-cost awake EEG recording that can be performed in a clinical office setting. The EEG data is processed through machine learning algorithms to achieve diagnostic accuracy comparable to polysomnography, dramatically reducing the cost of OSA diagnosis while maintaining measurement precision
Solution Approach 2:
The patent substitutes the complex mechanical and procedural system of overnight polysomnography with a computational approach using machine learning models. The system replaces physical overnight monitoring infrastructure with software-based analysis of awake EEG data, reducing both cost and complexity while preserving diagnostic accuracy
3Object-affected harmful factors
If alternative diagnostic methods are used instead of polysomnograms, then invasiveness and cost are reduced, but diagnostic accuracy deteriorates
Solution Approach 1:
The patent changes the parameters of EEG analysis by applying machine learning algorithms that extract sophisticated features from awake EEG data. Instead of using simple EEG metrics, the system transforms EEG signals through multiple processing stages including coherence analysis, entropy calculation, and pattern recognition, achieving high diagnostic accuracy with non-invasive awake recording
4Ease of operation
If traditional EEG analysis methods are used on awake data, then the procedure is simplified and less invasive, but diagnostic accuracy for sleep apnea deteriorates
Solution Approach 1:
The patent performs preliminary action by training machine learning models on large datasets of EEG data with known OSA diagnoses before applying the model to individual patients. This preliminary training phase allows the system to learn complex patterns associated with OSA, enabling accurate diagnosis during the actual clinical evaluation without requiring complex real-time analysis procedures
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between raw EEG data and diagnostic interpretation. This intermediary layer automatically extracts relevant features, identifies patterns, and makes predictions, simplifying the clinical workflow while achieving high diagnostic accuracy that would be difficult to obtain through traditional manual EEG analysis
Data Source
AI summary
A computer-implemented method and corresponding computer-based system detect sleep apnea. The computer-implemented method transforms electroencephalogram (EEG) data into features. The EEG data is produced from EEG signals output over a window of time while a patient is awake. The EEG signals are output by at least two EEG electrodes coupled to the patient. The computer-implemented method further detects sleep apnea in the patient by applying a prediction model to the features. The features represent electrodynamics of a brain of the patient as measured over the window of time via the at least two EEG electrodes while the patient is awake. Such a computer-implemented method and computer-based system obviate an overnight sleep study of the patient in order to diagnose the patient as having sleep apnea.