Oximetry Signal Classifier for Cheyne-Stokes Respiration Detection
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Solution Overview
Problem
Current methods for diagnosing Cheyne-Stokes Respiration (CSR) are costly, time-consuming, and require trained technicians, limiting accessibility and accuracy, especially in screening for sleep-disordered breathing disorders like CSR, OSA, and CSA, as they rely heavily on polysomnography and polygraphy, which are expensive and not readily available.
Innovation Solution
A classifier algorithm and diagnostic apparatus that uses oximetry signals, optionally in conjunction with nasal flow signals, to detect CSR by preprocessing the signals, calculating event and spectral features, and training a processor to produce probability values for CSR detection, enabling more accessible and cost-effective screening in a home setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography and polygraphy are used to diagnose CSR, then diagnostic accuracy is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent extracts the essential diagnostic function from complex polysomnography by using only oximetry signals to detect CSR. The classifier algorithm processes simplified oximetry data to identify CSR patterns, eliminating the need for full polysomnography equipment and trained technicians while maintaining diagnostic capability.
Solution Approach 2:
The patent employs inexpensive oximetry sensors and portable recording devices instead of expensive polysomnography equipment. The system uses affordable signal processing algorithms that can be implemented on standard computers or mobile devices, making the diagnostic tool accessible and cost-effective.
2Measurement precision
If polysomnography and polygraphy are used to diagnose CSR, then diagnostic accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the essential diagnostic function from complex polysomnography by using only oximetry signals to detect CSR. The classifier algorithm processes simplified oximetry data to identify CSR patterns, eliminating the need for full polysomnography equipment and trained technicians while maintaining diagnostic capability.
Solution Approach 2:
The patent replaces complex mechanical and electronic polysomnography equipment with a software-based classifier algorithm that runs on standard computers or mobile devices. The diagnostic functionality is implemented through signal processing software rather than specialized hardware, significantly reducing device complexity.
3Measurement precision
If trained technicians are required for diagnosis, then diagnostic accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements an automated classifier algorithm that performs diagnostic analysis without requiring trained technicians. The system automatically processes oximetry signals, applies classification rules, and generates diagnostic results, enabling non-experts to conduct accurate CSR screening in home or clinical settings.
Solution Approach 2:
The patent replaces the need for trained technicians with an automated software-based classifier algorithm. The diagnostic functionality is implemented through signal processing software that automatically analyzes oximetry data and identifies CSR patterns, eliminating the requirement for specialized human expertise.
Data Source
AI summary
Methods and apparatus provide Cheyne-Stokes respiration (“CSR”) detection based on a blood gas measurements such as oximetry. In some embodiments, a duration, such as a mean duration of contiguous periods of changing saturation or re-saturation occurring in an epoch taken from a processed oximetry signal, is determined. An occurrence of CSR may be detected from a comparison of the duration and a threshold derived to differentiate saturation changes due to CSR respiration and saturation changes due to obstructive sleep apnea. The threshold may be a discriminant function derived as a classifier by an automated training method. The discriminant function may be further implemented to characterize the epoch for CSR based on a frequency analysis of the oximetry data. Distance from the discriminant function may be utilized to generate probability values for the CSR detection.