Chronotropic Incompetence Detection via Sensor Validation
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Solution Overview
Problem
Current methods fail to accurately and timely detect chronotropic incompetence (CI), a condition that limits cardiac output and increases cardiovascular mortality, especially in patients with cardiovascular diseases or pacemakers, as they cannot effectively assess the heart's ability to increase heart rate with activity demands.
Innovation Solution
A device and method using sensor data to determine baseline cardiac health measures, validate activity levels, and detect CI by comparing cardiac health measures with baseline values, accounting for confounders like stress and medication, to provide a signal indicating CI through a processor-based system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current detection methods are used, then device complexity is reduced, but measurement precision of cardiac health measures deteriorates
Solution Approach 1:
The detection system is segmented into multiple independent sensor components (motion sensor, light sensor, audio sensor, temperature sensor) that each measure specific physiological parameters. This segmentation allows for precise measurement of individual parameters while keeping each sensor component relatively simple, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The processor performs multiple functions including validating activity levels, determining cardiac health measures, detecting chronotropic incompetence, and accounting for confounders. This multi-functionality consolidates complex detection capabilities into a single integrated system, improving measurement precision without proportionally increasing overall device complexity.
2Measurement precision
If baseline cardiac health measures are determined based on activity level estimates, then measurement precision improves, but loss of time increases due to validation requirements
Solution Approach 1:
The system performs preliminary validation of activity level estimates using sensor data before determining baseline cardiac health measures. By validating activity levels in advance, the system ensures measurement precision while streamlining the overall process, reducing time loss compared to post-measurement verification.
Solution Approach 2:
The system uses sensor data to provide feedback on activity level estimates, validating them against actual physiological responses. This feedback mechanism allows for rapid verification of activity levels, improving the accuracy of baseline measures while minimizing time loss through efficient real-time validation.
3Measurement precision
If confounders such as stress and medication are accounted for, then measurement precision improves, but device complexity increases
Solution Approach 1:
The processor is designed to perform multiple functions including detecting confounders such as stress and medication effects alongside the primary function of detecting chronotropic incompetence. This multi-functionality allows the system to account for confounders without requiring separate dedicated systems, improving measurement precision while controlling device complexity through integrated design.
Data Source
AI summary
Detecting chronotropic incompetence includes determining, using a processor, a baseline cardiac health measure for a user based upon an estimate of activity level for the user, validating, using the processor, the estimate of activity level for the user with a validation factor determined from sensor data and determining, using the processor, a cardiac health measure for the user from the sensor data. A signal indicating chronotropic incompetence for the user can be provided by the processor based upon a comparison of the cardiac health measure for the user with the baseline cardiac health measure.