Sleep CPC Heart Rate Analysis for Cardiovascular Health Screening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sleep analysis technologies, including cardiopulmonary coupling, primarily focus on sleep quality and sleep disorders, with limited applications beyond these areas.
Innovation Solution
A system and method for evaluating health conditions using cardiopulmonary coupling data and heart rate data during sleep, involving the identification of high and low frequency coupling states, fitting curves to heart rate data, and performing multiple linear regression to assess health conditions based on heart rate trends and differences between coupling states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cardiopulmonary coupling data is used for sleep quality and sleep disorder analysis, then evaluation accuracy for sleep parameters is improved, but applicability to other health conditions remains limited
Solution Approach 1:
The patent extends the application of cardiopulmonary coupling data from traditional sleep analysis to broader health condition evaluation. By analyzing heart rate patterns during high and low frequency coupling states, the system can assess cardiovascular health, detect health concerns, and provide evaluations beyond sleep parameters, thereby achieving multi-functionality and improved versatility.
2Reliability
If heart rate data is analyzed during high frequency coupling states, then detection of cardiovascular health issues is improved, but complexity of data processing increases
Solution Approach 1:
The patent segments the sleep period into distinct high frequency coupling states and low frequency coupling states based on cardiopulmonary coupling data. This segmentation allows for targeted analysis of heart rate patterns during specific physiological states, improving detection accuracy while managing processing complexity through structured data organization.
Solution Approach 2:
The system changes the analysis parameters by focusing on heart rate patterns specifically during high frequency coupling states rather than analyzing all sleep data uniformly. This parameter-specific approach improves detection reliability for cardiovascular issues while simplifying the overall processing strategy by concentrating on critical time periods.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
In accordance with one aspect, a system for evaluating a health condition includes a storage containing cardiopulmonary coupling (CPC) data and heart rate data for a person for a sleep time period, one or more processors, and a memory storing instructions. When the instructions are executed by the processor(s), they cause the system to access the CPC data and the heart rate data from the storage, identify one or more time periods in the sleep time period categorized as high frequency coupling (HFC) state based on the CPC data, evaluate one or more characteristics of a portion of the heart rate data corresponding to the one or more time periods, and evaluate health condition of the person based on the one or more characteristics of the portion of the heart rate data corresponding the one or more time periods categorized as HFC state.