Wearable Sleep Apnea Detection Using SpO2 Data Merging
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Solution Overview
Problem
Sleep apnea is under-detected due to the requirement for specialized equipment and personnel, making it costly and inaccessible for direct consumer detection.
Innovation Solution
A wearable device with a hardware processor continuously monitors sensor signals to detect sleep sessions, estimate the apnea-hypopnea index (AHI) using SpO2 data, and generate an indication of sleep apnea based on threshold comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography (PSG) with specialized equipment is used to detect sleep apnea, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses a wearable device that copies the essential measurement function of PSG by monitoring SpO2 levels, heart rate, and movement patterns to estimate AHI. Instead of requiring full polysomnography equipment, the invention creates a simplified copy that captures the critical indicators of sleep apnea through consumer-grade sensors, achieving acceptable detection accuracy without specialized equipment.
Solution Approach 2:
The invention extracts the key diagnostic elements from full PSG (SpO2 monitoring, heart rate detection, movement tracking) and isolates them into a minimal viable system. By taking out only the essential measurement components needed to estimate AHI and removing unnecessary PSG elements, the patent creates a streamlined device that maintains diagnostic relevance while reducing complexity and cost.
2Reliability
If polysomnography (PSG) is performed at a clinic with specialized personnel, then reliability of diagnosis is improved, but ease of operation deteriorates due to required medical infrastructure
Solution Approach 1:
The wearable device enables users to self-monitor their sleep apnea risk without requiring clinic visits or specialized personnel. The device automatically collects data throughout the night, processes it locally, and generates an eAHI estimate that users can interpret themselves. This self-service approach maintains reliability through automated algorithms while dramatically improving ease of operation by eliminating the need for medical infrastructure.
Solution Approach 2:
The patent changes the operational parameters from clinic-based scheduled appointments to continuous at-home monitoring. By shifting from intermittent professional evaluation to continuous automated measurement, the invention improves accessibility while maintaining diagnostic reliability through persistent data collection and algorithmic analysis of sleep patterns, SpO2 variations, and respiratory events.
3Measurement precision
If multiple short sleep sessions are monitored separately, then measurement precision for each session is maintained, but productivity decreases due to insufficient data for reliable eAHI calculation
Solution Approach 1:
The patent merges multiple short sleep sessions into a consolidated dataset for eAHI calculation. When the device detects that a sleep session is insufficient (shorter than the required 10-minute minimum or interrupted), it combines data from adjacent sleep periods within the same monitoring cycle. This merging approach accumulates sufficient data points to enable reliable eAHI estimation while maintaining measurement precision by applying the same analytical criteria to the aggregated dataset.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and reliable detection of sleep apnea in a non-invasive, cost-effective manner, providing a direct-to-consumer solution without the need for specialized equipment or personnel.
Implementation Method 1
measuring, by the hardware processor and based on the sensor signals, amounts of valid SpO2 data collected for the plurality of sleep sessions
Data Source
AI summary
While a user is wearing a device during one or more sleep cycles, sensor signals are continuously monitored to detect a plurality of sleep sessions occurring during the one or more sleep cycles. Based on the sensor signals, amounts of valid SpO2 data collected for the plurality of sleep sessions are measured. A merged sleep session is generated by combining selected sleep sessions of the plurality of sleep sessions occurring in a same sleep cycle based on a maximum amount of time between the selected sleep sessions and the amounts of valid SpO2 data collected for the selected sleep sessions. An estimated apnea hypopnea index (eAHI) is estimated for the one or more sleep sessions including the merged sleep session. An indication of whether the user has sleep apnea is generated based on one or more of the eAHIs for the one or more sleep cycles.