Audio-Based Sleep Disordered Breathing Screening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current screening tools for Sleep Disordered Breathing (SDB) conditions are costly and impractical for widespread use, relying heavily on snoring data and requiring physician interaction, which limits their effectiveness in early detection and awareness among the general public.
Innovation Solution
A user-friendly, cost-effective SDB screening tool utilizing a processor and microphone to analyze audio signals during sleep, identifying reliable respiration epochs and detecting SDB events such as apnea and hypopnea without the need for physician support, through algorithms that distinguish between breathing, snoring, and background noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional screening tools for Sleep Disordered Breathing are used, then measurement precision may be adequate, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts and isolates only the essential audio signal processing functions needed for SDB detection, separating them from the complex polysomnography system. By focusing solely on audio analysis rather than multiple physiological parameters, the system achieves adequate detection accuracy with significantly reduced complexity.
Solution Approach 2:
The invention replaces expensive, complex medical equipment with inexpensive consumer electronics (smartphone microphone and processor). The system uses readily available, low-cost components to perform screening functions that previously required sophisticated medical devices.
2Reliability
If traditional screening tools requiring physician interaction are used, then diagnostic reliability may be maintained, but ease of operation and accessibility deteriorate
Solution Approach 1:
The system enables users to perform self-screening for Sleep Disordered Breathing using their own smartphone. The automated algorithm processes audio recordings and provides results without requiring physician interaction, making the screening process self-service and significantly more accessible to the general public.
Solution Approach 2:
The patent performs preliminary screening and filtering of cases before they reach medical professionals. By automatically analyzing audio signals and identifying potential SDB cases, the system prepares and pre-screens data, allowing physicians to focus only on cases that require their expertise.
3Ease of operation
If snoring-based screening methods are used, then ease of operation is maintained, but measurement precision and reliability worsen due to inability to distinguish snoring from breathing
Solution Approach 1:
The patent applies different analysis methods to different portions of the audio signal. By analyzing specific acoustic characteristics and temporal patterns in different segments of the recording, the system can distinguish between snoring and actual breathing events, improving measurement precision while maintaining operational simplicity.
Solution Approach 2:
Instead of trying to detect breathing directly and filter out snoring, the system inverts the approach by using snoring detection as a reference pattern and identifying deviations from this pattern that indicate actual breathing events. This inverted methodology improves accuracy in distinguishing between the two sound types.
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 dependable detection of SDB events, providing users with indicators and statistics, promoting early awareness and potential treatment of SDB conditions without the need for extensive medical intervention.
Implementation Method 1
receiving, in a processor, an input audio signal representing sounds of the user
Data Source
AI summary
Methods and apparatus detect events of sleep disordered breathing from an input audio signal such as from a sound sensor. A processor may be configured to receive the audio signal that represents sounds of a user during a period of sleep. The processor determines reliable respiration epochs from the audio, such as on a frame-by-frame basis, that include periods of audible breathing and/or snoring. The processor detects presence of a sleep disordered breathing events, such as hypopnea, apnea, apnea snoring or modulated breathing, in the reliable respiration epoch(s) and generates output to indicate the detected event(s). Optionally, the apparatus may serve as a cost-effective screening device such as when implemented as a processor control application for a mobile processing device (e.g., mobile phone or tablet).