Minute Ventilation Signal Resampling for Disordered Breathing Detection
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Solution Overview
Problem
Current methods for detecting disordered breathing, such as sleep apnea and hypopnea, are inadequate as they often result in fragmented sleep and can lead to serious health consequences due to their inability to accurately monitor respiratory patterns, especially during REM sleep when autonomic activity increases, and they require patients to spend nights in a sleep laboratory for diagnosis.
Innovation Solution
The development of a minute ventilation-based detection methodology that uses breath intervals and tidal volume measurements to produce an unevenly sampled instantaneous minute ventilation signal, which is then resampled to create an evenly sampled signal, allowing for the detection of disordered breathing by comparing it to a baseline threshold, and includes a respiration cycle quality check to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional disordered breathing detection methods are used, then diagnosis can be performed, but the detection accuracy is insufficient and results in fragmented sleep due to inability to accurately monitor respiratory patterns
Solution Approach 1:
The patent transforms the respiratory signal from its original form into a minute ventilation signal by applying mathematical operations (integration and differentiation). This parameter transformation enhances the ability to detect disordered breathing events by emphasizing relevant physiological changes while filtering out noise, thereby improving detection accuracy without causing false arousals
Solution Approach 2:
The patent replaces conventional mechanical or simple threshold-based detection methods with a sophisticated signal processing system that uses minute ventilation calculation. This substitution enables more accurate detection of respiratory disturbances by analyzing the derivative of the integrated respiratory signal, which better reflects actual minute ventilation changes during sleep
2Measurement precision
If sleep laboratory monitoring is used for diagnosis, then accurate detection can be achieved, but it requires patients to spend nights in a sleep laboratory which is inconvenient and resource-intensive
Solution Approach 1:
The patent enables the monitoring system to automatically perform detection and analysis of disordered breathing events using algorithms that calculate minute ventilation signals from recorded respiratory data. This self-service capability allows accurate diagnosis to be performed at home without requiring sleep laboratory personnel or equipment, making the process convenient for patients while maintaining diagnostic accuracy
Solution Approach 2:
The patent introduces a minute ventilation signal as an intermediary representation of the respiratory pattern. This intermediate signal serves as a bridge between the raw respiratory measurement and the final diagnosis, enabling accurate detection to be performed by simple comparison against threshold values, thus allowing home-based monitoring with laboratory-quality accuracy
3Device complexity
If simple threshold-based detection is used, then the system is easy to implement, but it produces false positives and lacks sensitivity in detecting disordered breathing events
Solution Approach 1:
The patent transforms the respiratory signal into a minute ventilation signal through mathematical operations, fundamentally changing the parameter being analyzed. This transformation converts complex respiratory patterns into a simplified signal where disordered breathing events appear as distinct deviations from baseline, maintaining system simplicity while dramatically improving detection sensitivity and reducing false positives
Data Source
AI summary
A respiration pattern of a number of respiration cycles is detected and breath intervals (BI) and tidal volume (TVOL) measurements of each of the respiration cycles are respectively determined. An unevenly sampled instantaneous minute ventilation (iMV) signal is produced using the BI and TVOL measurements, and an evenly sampled iMV signal (resampled iMV signal) is produced using the unevenly sampled iMV signal. Disordered breathing is detected based on a comparison between a baseline threshold and the resampled iMV signal.