Respiratory Flow Half-Cycle Detection for M-Wave CPAP Control
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Solution Overview
Problem
Existing methods for detecting obstructive sleep apnea (OSA) fail to accurately identify flow limitation patterns, particularly the M-wave pattern, and are computationally expensive, limiting their use in low-cost electronic and software platforms.
Innovation Solution
A simplified algorithm for detecting flow limitation in respiratory airflow patterns, using improved flow flattening indices and curvature analysis, specifically designed for low-end electronic platforms, which identifies the M-wave pattern and adjusts CPAP treatment pressure accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods for detecting M-wave pattern with overshoots are used, then detection capability is provided, but measurement precision and resource consumption are inadequate
Solution Approach 1:
The inspiratory airflow signal is divided into multiple segments: pre-overshoot, overshoot, and post-overshoot regions. This segmentation allows selective analysis of the central portion while normalizing or excluding the overshoot regions, improving detection precision without requiring complex full-signal processing.
Solution Approach 2:
The overshoot portion of the airflow signal is extracted and separately handled through normalization. By isolating this problematic region and applying specific normalization techniques, the method removes the confounding effect of overshoots on obstruction detection, thereby improving measurement precision.
2Reliability
If comprehensive airflow analysis is performed to detect all obstruction patterns, then detection coverage is improved, but computational resources increase
Solution Approach 1:
Different processing strategies are applied to different portions of the airflow signal. The central portion undergoes detailed flattening index calculation for reliable obstruction detection, while the overshoot portions are normalized or excluded. This localized quality approach maintains detection reliability while reducing overall computational burden.
Solution Approach 2:
The method applies partial action by focusing computational resources only on the most informative central portion of the inspiratory signal rather than analyzing the entire waveform. This selective approach provides sufficient detection reliability for clinical purposes while significantly reducing processing power consumption.
Data Source
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AI summary
The present invention discloses an apparatus for detecting inspiratory and expiratory flow-time curves in an airflow, comprising: a flow detector configured to generate an airflow signal representing a respiratory airflow curve based on measurements of the airflow overtime, and a processor in communication with the flow detector, the processor configured to: process the airflow signal by: provisionally taking a start of inspiration as a time that the airflow exceeds a predetermined threshold, rejecting the start of inspiration if the airflow then falls below zero before a total volume inspired reaches a first predetermined value, provisionally taking a start of expiration as a time that the airflow falls below zero, and rejecting the start of expiration if the airflow then goes above a second predetermined value in an inspiratory direction before a total volume expired reaches a predetermined value; and adjust, based on the processed airflow signal, a treatment pressure provided to a patient.