Sleep Event Detection Using Machine Learning Sound Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying sleep issues are often invasive, expensive, and time-consuming, requiring laboratory tests that may not accurately replicate a person's home environment, leading to undiagnosed or unresolved sleep problems.
Innovation Solution
A system using machine learning algorithms to analyze sound data collected during sleep intervals by comparing subject sound characteristics to sample sound characteristics associated with known sleep events, allowing for the identification of sleep events such as sleep apnea or insomnia without the need for laboratory tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laboratory tests are used to identify sleep issues, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a simplified copy of laboratory-grade sleep analysis by training machine learning models on laboratory sleep study data. The model learns to replicate expert sleep specialist decision-making using only ambient sound recordings, eliminating the need for complex laboratory equipment while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces the mechanical and physical systems used in laboratory sleep tests (sensors, monitoring equipment, complex apparatus) with a computational system based on machine learning algorithms that process ambient sound data, thereby reducing device complexity while preserving measurement precision
2Measurement precision
If laboratory tests are used to identify sleep issues, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by training the machine learning model in advance on extensive laboratory sleep study data. Once trained, the model can instantly analyze ambient sound recordings without requiring time-consuming laboratory procedures, thereby reducing loss of time while maintaining measurement precision through the pre-acquired knowledge
3Ease of operation
If machine learning analysis of sound data is used, then ease of operation is improved, but measurement precision may worsen
Solution Approach 1:
The patent performs preliminary training of the machine learning model on large datasets of labeled sleep events from laboratory studies. This pre-training ensures that when the system is operated, it already possesses high measurement precision without requiring complex user configuration or adjustment, thus maintaining both ease of operation and accuracy
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously improves by learning from additional labeled data and user corrections. This allows the system to maintain high measurement precision while remaining easy to operate, as the model adapts to improve accuracy automatically rather than requiring complex user intervention
Data Source
AI summary
A computer system for assessing sound to analyze a user's sleep includes a processor configured to perform operations including: (i) storing sample sound data associated with a plurality of sample sleep events, the sample sound data including a plurality of sample characteristics each respectively associated with at least one sample sleep event of the plurality of sample sleep events; (ii) receiving, from a client device, subject sound data collected during a sleep interval; (iii) analyzing, using a machine learning algorithm, the subject sound data collected during the sleep interval; (iv) identifying, based upon the analyzing, a subject characteristic associated with the subject sound data; (v) comparing the subject characteristic with the plurality of sample characteristics; and (vi) determining, based upon the comparing, whether the subject characteristic substantially matches at least one sample characteristic to identify one or more subject sleep events occurring during the sleep interval.