Cardiopulmonary Coupling Analysis for REM and Wake State Designation
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Solution Overview
Problem
Existing sleep analysis technologies struggle to accurately designate REM and WAKE states during a sleep period using cardiopulmonary coupling data without relying on non-CPC physiological data.
Innovation Solution
A method and system that analyze cardiopulmonary coupling data to identify epochs of very-low frequency coupling, utilizing dynamic thresholds and frequency band analysis to designate REM and WAKE states, and optionally incorporate actigraphy, pseudo-actigraphy, and physiological data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cardiopulmonary coupling data alone is used to designate REM and WAKE states, then device complexity is reduced and ease of operation is improved, but measurement precision deteriorates because REM and WAKE have very similar CPC characteristics
Solution Approach 1:
The patent segments the vLFC epoch designation process into multiple sub-steps: (1) identifying vLFC epochs using CPC data, (2) obtaining ancillary physiological data for those epochs, (3) applying a decision algorithm that integrates both CPC and ancillary data characteristics, and (4) designating REM versus WAKE states based on the combined analysis. This segmentation allows the system to maintain simplicity in the primary CPC measurement while adding precision through targeted use of supplementary data only when needed.
Solution Approach 2:
The patent introduces ancillary physiological data as an intermediary element that mediates between the limited discriminative power of CPC data alone and the need for accurate REM/WAKE distinction. The ancillary data (such as ECG, EEG, or other physiological signals) serves as a bridge, providing additional characteristics that help differentiate REM from WAKE states during vLFC epochs without requiring a complete shift to more complex measurement systems.
2Measurement precision
If ancillary physiological data is incorporated to improve REM and WAKE state classification accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies partial action by using ancillary physiological data selectively rather than continuously. The system uses CPC data for all epochs and only incorporates ancillary data specifically during vLFC epochs where REM/WAKE differentiation is challenging. This partial use of additional data provides sufficient precision improvement for the critical ambiguous cases without the full complexity burden of continuous multi-parameter monitoring.
Solution Approach 2:
The patent implements a dynamic data integration approach where the system adapts its data usage based on the epoch characteristics. During stable sleep epochs, only CPC data is used, maintaining simplicity. During vLFC epochs, the system dynamically incorporates ancillary physiological data to improve accuracy. This dynamic adjustment of data integration strategy allows the system to optimize the balance between precision and complexity based on real-time sleep state characteristics.
Data Source
AI summary
The present disclosure provides systems and method of analyzing whether a sleep epoch is a REM sleep epoch or a wake epoch. In accordance with aspects of the present disclosure, a computer-implemented method includes accessing cardiopulmonary coupling data spanning a sleep period for a person, identifying an epoch in the sleep period corresponding to very-low frequency coupling in the cardiopulmonary coupling data, accessing high-frequency coupling data and/or low-frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch, and designating the epoch as a REM sleep epoch or as a wake epoch based on the high-frequency coupling data and/or the low-frequency coupling data corresponding to the epoch, where the epoch is designated based on the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.