Wearable SAS Screening via Sensor Stream Processing
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Solution Overview
Problem
Conventional methods for screening Sleep Apnea Syndrome (SAS) are cumbersome, costly, and often fail to detect the condition in a timely and accurate manner due to the need for polysomnography tests that disrupt normal sleep rhythms and require specialized equipment and facilities.
Innovation Solution
A wearable device with embedded sensors and processing capabilities that continuously detect physiological signals, such as ECG and acceleration, and utilize machine learning algorithms to predict SAS risk levels, providing out-of-center screening in a more comfortable and cost-effective manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography test is used for SAS screening, then measurement precision is improved, but device complexity and loss of time are worsened
Solution Approach 1:
The patent extracts the essential SAS detection function from the complex polysomnography system by using only necessary sensors (accelerometer, microphone, ECG) to detect apnea events, eliminating the need for comprehensive sleep monitoring equipment while maintaining diagnostic accuracy
Solution Approach 2:
The patent creates a simplified copy of the polysomnography testing process that can be performed at home using wearable devices, replicating the core detection capabilities without requiring specialized sleep center facilities and expert interpretation
2Measurement precision
If polysomnography test is used for SAS screening, then measurement precision is improved, but loss of time is worsened
Solution Approach 1:
The patent performs preliminary detection and processing of physiological signals during normal sleep at home, with data automatically analyzed by machine learning algorithms that continuously monitor for apnea events without requiring scheduled testing appointments or overnight stays at sleep centers
Solution Approach 2:
The wearable device continuously monitors physiological parameters throughout the night during natural sleep cycles, providing ongoing detection without interruption, whereas traditional polysomnography requires discrete testing sessions that disrupt sleep patterns
3Measurement precision
If polysomnography test is used for SAS screening, then measurement precision is improved, but use of energy by stationary object is worsened
Solution Approach 1:
The patent enables patients to perform self-monitoring at home using wearable devices that automatically collect and process data without requiring expensive sleep center facilities, specialized equipment, or professional interpretation services, thereby eliminating significant testing costs while maintaining detection accuracy
4Ease of operation
If conventional SAS screening methods are used, then ease of operation is improved, but measurement precision is worsened
Solution Approach 1:
The patent replaces manual clinical assessment and expert interpretation with automated machine learning algorithms that objectively analyze physiological data, eliminating subjectivity and improving detection accuracy while maintaining the convenience of home-based testing
Data Source
AI summary
A method and system for Sleep Apnea Syndrome (SAS) screening are disclosed. The method comprises detecting at least one physiological signal, converting the at least one physiological signal into at least one sensor stream, and processing the at least one sensor stream to perform the SAS screening. The system includes a sensor to detect at least one physiological signal, a processor coupled to the sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to convert the at least one physiological signal into at least one sensor stream and to processor the at least one sensor stream to perform the SAS screening.