Seismocardiography VO2max Estimation Using Aortic Valve Closure
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Solution Overview
Problem
There is a need for a low-cost and portable technology that can provide an indication of cardiorespiratory fitness, particularly VO2max, which existing technologies have not adequately addressed.
Innovation Solution
A method and system utilizing a seismocardiogram (SCG) recorded with an accelerometer to determine properties of the aortic valve closure (AC) signal feature, combined with machine learning models, to quantify cardiorespiratory fitness, including features like amplitude, time separation, morphology, and frequency measures, and demographic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional VO2max testing methods are used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the essential feature (aortic valve closure signal) from the complex seismocardiogram signal and uses it as a simplified proxy for VO2max assessment. By focusing on a single key signal characteristic rather than comprehensive cardiovascular monitoring, the system achieves accurate fitness estimation with minimal equipment.
Solution Approach 2:
The patent replaces complex mechanical VO2max testing equipment with a simple accelerometer-based seismocardiography system. Instead of using metabolic carts, gas analysis systems, or comprehensive exercise physiology monitors, the invention uses chest wall vibrations to infer cardiovascular function and fitness level.
2Measurement precision
If multiple signal features are analyzed to improve accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the seismocardiogram signal into distinct phases and identifies specific fiducial points (aortic valve closure, mitral valve closure, etc.). By dividing the complex continuous signal into discrete, meaningful segments with identifiable landmarks, the system simplifies analysis while maintaining precision in fitness assessment.
Solution Approach 2:
The patent focuses analysis on specific local features of the SCG signal (such as the aortic valve closure peak) rather than treating the entire signal uniformly. By concentrating on locally significant characteristics that have proven correlation with VO2max, the system achieves accurate fitness estimation without processing every signal component equally.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively predicts VO2max with improved accuracy, demonstrating high correlation with traditional VO2max tests, indicating its effectiveness in assessing cardiorespiratory fitness.
Implementation Method 1
obtaining a seismocardiogram (SCG) recorded with an accelerometer configured to measure accelerations and vibrations of the chest wall of a person caused by myocardial movement
Implementation Method 2
The signal from the accelerometer is then typically filtered such that it does not contain any audible components. If the accelerometer signal is low pass filtered, for example with an upper cutoff of 40 Hz, the influence of heart sounds is removed.
Data Source
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Figure 3a~4b
AI summary
The proposed technology relates to the quantifying of cardiorespiratory fitness. It includes the obtaining (102) of a seismocardiogram (SCG) recorded with an accelerometer (14) configured to measure accelerations and vibrations of the chest wall of a person (18) caused by myocardial movement. Properties of a first signal feature (AC) in the seismocardiogram (SCG) are determined (104), wherein the first signal feature (AC) corresponds to the aortic valve closure (AC) of a heartbeat. A measure indicating cardiorespiratory fitness (VO2max) is then determined (106) based on the properties of first signal feature (AC).