Wearable OSA Detection via Breathing Pattern Analysis
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Solution Overview
Problem
Current methods for detecting and managing obstructive sleep apnea (OSA) are limited by their inability to provide real-time data analysis, require cumbersome and invasive equipment, and offer little insight into the severity of sleep disturbances beyond the Apnea/Hypopnea Index (AHI), which does not account for varying sleep patterns or comorbidities, leading to delayed and incomplete treatment.
Innovation Solution
A non-invasive, wearable system that combines biometric sensors and machine learning to detect OSA episodes in real-time, providing a comprehensive Sleep Disturbance Severity Index (SDSI) that includes metrics for apnea and hypopnea events, allowing for immediate notification and potential treatment through nerve stimulation or other interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography (PSG) is used to detect OSA episodes, then measurement precision is improved, but device complexity and ease of operation worsen due to cumbersome equipment and professional interpretation requirements
Solution Approach 1:
The patent extracts the essential detection function from complex PSG equipment by using a simple wearable device that monitors breathing patterns, oxygen saturation, and other vital signs through minimal sensors, eliminating the need for complex laboratory equipment while maintaining detection capability
Solution Approach 2:
The patent creates a simplified copy of PSG functionality by using machine learning models trained on PSG data to replicate professional interpretation capabilities in a consumer wearable device, allowing automated analysis without requiring sleep specialists
2Measurement precision
If polysomnography (PSG) is used to detect OSA episodes, then measurement precision is improved, but ease of operation worsens due to uncomfortable equipment
Solution Approach 1:
The patent removes uncomfortable PSG equipment (wires, multiple sensors, lab setting) and retains only the essential monitoring functions through a simple wearable device that can be used at home without disrupting normal sleep
Solution Approach 2:
The patent employs a disposable or easily replaceable sensor patch that is comfortable for overnight wear, eliminating the need for durable, complex medical equipment while providing sufficient monitoring capability for a single night's data collection
3Productivity
If Apnea/Hypopnea Index (AHI) is used to assess sleep disturbances, then productivity is improved by providing a single metric, but loss of information worsens by not accounting for varying sleep patterns and comorbidities
Solution Approach 1:
The patent segments the single AHI metric into multiple distinct metrics including apnea events, hypopnea events, oxygen desaturation events, and arousal events, allowing separate analysis of different sleep disturbance types while maintaining overall assessment efficiency
Solution Approach 2:
The patent adds temporal dimension by analyzing sleep disturbances across different sleep stages (REM, non-REM, deep sleep) and provides longitudinal tracking over multiple nights, transforming the static AHI into a multi-dimensional profile that captures variation in sleep patterns and severity
4Measurement precision
If polysomnography (PSG) is used to detect OSA episodes, then measurement precision is improved, but loss of time worsens due to delayed results delivery
Solution Approach 1:
The patent implements automated analysis algorithms that process sleep data and generate diagnostic reports without requiring professional interpretation, allowing the device to serve itself in analyzing results and delivering reports automatically within hours rather than days
Solution Approach 2:
The patent replaces the manual mechanical process of professional PSG interpretation with automated computational algorithms and machine learning models that can rapidly analyze data and generate results without human intervention in the analysis phase
Data Source
AI summary
Example inventions detect and treat an occurrence of an apnea event during a sleep period of a user. Example inventions receive data corresponding to nasal air pressure of the user and/or audio of the user during the sleep period. Example inventions determine patterns in the data, based on the patterns, detect the apnea event and, in response to the detecting, treat the apnea event.