Periodic Breathing Detection in CPAP Therapy
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Solution Overview
Problem
Current breathing therapy devices lack sensitivity in detecting periodic breathing patterns and fail to distinguish between mild and severe cases, which can lead to inadequate adjustment of therapy delivery.
Innovation Solution
The use of signal processing techniques, including Hilbert transforms and ranking filters, to detect and categorize periodic breathing patterns, allowing for more sensitive detection and severity grading, enabling tailored therapy adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signal processing techniques such as Hilbert transforms and ranking filters are used, then detection sensitivity and severity classification capability are improved, but device complexity increases
Solution Approach 1:
The patent segments the breathing signal analysis into distinct processing stages: raw signal acquisition, Hilbert transform for envelope detection, ranking filter application for discontinuity removal, and severity classification. This segmentation allows complex signal processing to be broken down into manageable, sequential operations that improve detection sensitivity while maintaining systematic control over device complexity
Solution Approach 2:
The patent introduces intermediate processing signals and variables as mediators between the raw breathing signal and the final detection output. The Hilbert transform produces an intermediate envelope signal, which then feeds into the ranking filter, which in turn provides input for severity classification. These intermediary processing steps enable sophisticated analysis while structuring the complexity in a controlled manner
2Device complexity
If current prior art devices are used for periodic breathing detection, then device simplicity is maintained, but detection sensitivity and ability to distinguish severity levels deteriorates
Solution Approach 1:
The patent changes key processing parameters by introducing the Hilbert transform to extract the envelope of the breathing signal, applying ranking filters with specific window parameters to handle discontinuities, and establishing severity thresholds based on envelope characteristics. These parameter changes transform simple detection into sensitive, severity-differentiating detection while maintaining a relatively straightforward implementation approach
Data Source
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AI summary
An improvement to a breathing therapy machine to detect and rate occurrences of periodic breathing and to alter the therapy delivered to a user of the device based on the rating. Detecting periodic breathing in a subject using a breathing therapy device is useful to ensure proper pressure therapy is administered to the subject, and to determine the ongoing suitability of breathing therapy for the subject.