Vibration-Based OSAHS Detection Device for Snoring Analysis
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Solution Overview
Problem
Conventional OSAHS detection devices rely on sound waves, which lead to low specificity due to interference from speaking or external sounds, resulting in inaccurate snoring condition assessments.
Innovation Solution
A method and device using a bedding with a microprocessor, micro signal sensor, and memory to detect OSAHS by acquiring and comparing vibration signals within specific frequency ranges associated with inspiration and expiration, determining snoring based on signal strength ratios and thresholds, and filtering out high-frequency signals synchronized with breathing phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sound wave detection is used to detect snoring, then the detection can be implemented, but the specificity is low due to interference from speaking or external sounds
Solution Approach 1:
The patent replaces acoustic detection (sound wave-based) with vibration detection. The vibration detection device detects vibrations generated by snoring through contact with the subject's body or bedding, thereby eliminating interference from external sounds and speaking that plague acoustic methods.
Solution Approach 2:
The patent introduces a vibration detection device as an intermediary between the snoring subject and the detection system. This intermediary converts acoustic energy of snoring into mechanical vibrations that can be detected and analyzed, filtering out non-snoring sounds in the process.
2Measurement precision
If vibration signals are analyzed to improve detection accuracy, then measurement precision improves, but device complexity increases due to signal processing requirements
Solution Approach 1:
The patent segments the vibration signal analysis into distinct frequency components. By analyzing specific frequency ranges associated with breathing phases (inspiration and expiration), the system achieves high accuracy without requiring complex full-spectrum analysis. The signal processing is divided into manageable frequency bands.
Solution Approach 2:
The patent changes the detection parameter from general sound intensity to specific vibration frequency characteristics. By focusing on frequency-domain parameters rather than time-domain sound levels, the system achieves high precision with relatively simple processing. The analysis compares vibration strengths at different frequencies during different breathing phases.
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
This approach significantly increases the accuracy of OSAHS detection by synchronizing with breathing movements, reducing interference from coughing and speaking, and allowing for minor software modifications without additional hardware costs, thereby improving snoring status identification.
Implementation Method 1
acquiring a vibration signal of a subject during sleep
Data Source
AI summary
A method and a device for detecting OSAHS provided that the method comprises: acquiring a vibration signal of a subject during sleep, and determining a breathing signal of the subject (S1), wherein the breathing signal comprises an inspiration signal generated upon inspiration and an expiration signal generated upon expiration; acquiring strength of a first vibration signal within a specified frequency range and superimposed on the inspiration signal, and strength of a second vibration signal within a specified frequency range and superimposed on the expiration signal adjacent to the inspiration signal (S2); and comparing, according to a preset method, the strength of the first vibration signal with the strength of the second vibration signal, and determining, according to a comparison result, whether the subject is snoring (S3). Since the detection is performed synchronously with breathing, the invention can prevent interference caused by coughing, speaking and other acoustic signals transmitted in the air, thereby significantly increasing accuracy in determining OSAHS. Moreover, the method and device of the invention can be realized by only making a minor modification to software in existing sleep sensors without incurring additional hardware costs.