Arterial Disease Detection via Acoustic Eigen Function Analysis
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Solution Overview
Problem
Current methods for detecting arterial disease are inconsistent and may fail to detect blockages, as they rely on human hearing limitations and are not effective for all frequencies and volumes of sound associated with blood vessel abnormalities.
Innovation Solution
A non-invasive system that uses acoustic signals from sensors positioned near arteries to generate a complex frequency grid, analyzing peak-perturbation and line-perturbation acoustic signals to predict arterial disease through predictive models, identifying characteristic frequencies and lifetimes associated with early and late-stage arterial disease.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human hearing is used to detect bruits, then the detection method is simple, but the detection reliability is poor due to frequency and volume limitations
Solution Approach 1:
The patent replaces the mechanical human hearing system with an electronic acoustic sensor system. The sensor captures acoustic signals across a wide frequency range, and a computer processes these signals to detect arterial disease markers, eliminating the limitations of human auditory perception while maintaining operational simplicity.
Solution Approach 2:
The system changes the detection parameters by extending the frequency range from human audible limits to a broader spectrum including ultrasonic frequencies. The computer analysis processes acoustic signals with varying frequencies and volumes to identify characteristic patterns of arterial disease, improving detection reliability through enhanced parameter coverage.
2Measurement precision
If acoustic signals are analyzed with a complex frequency grid, then the detection precision is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the acoustic signal analysis into distinct components: frequency grid generation, lifetime calculation, and pattern recognition. The complex frequency grid is divided into manageable frequency points, each with associated lifetimes, allowing systematic analysis that improves precision while maintaining computational tractability through structured processing.
Solution Approach 2:
The computer acts as an intermediary between the acoustic sensor and the diagnostic conclusion. It processes the raw acoustic signals through the complex frequency grid analysis, identifies characteristic patterns associated with arterial disease, and provides diagnostic information, thereby achieving high measurement precision through automated computational mediation.
3Reliability
If predictive models are used to detect early stage disease, then the detection capability is enhanced, but the model complexity increases
Solution Approach 1:
The patent applies preliminary action by developing predictive models that can detect early stage arterial disease before severe blockages occur. The models analyze acoustic signal patterns at early stages, enabling intervention before critical conditions develop. This preliminary detection capability enhances reliability by catching disease at more manageable stages.
Solution Approach 2:
The system incorporates feedback mechanisms where the computer continuously analyzes acoustic signals, compares them against the predictive models, and adjusts its detection criteria. This feedback loop refines the model application in real-time, enhancing detection capability for early stage disease while managing model complexity through adaptive processing.
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
This system enables accurate detection of arterial disease by analyzing specific acoustic signals, overcoming the limitations of human hearing and providing a reliable method for identifying blockages through predictive models of complex frequencies.
Implementation Method 1
Acoustic signals are obtained from a sensor held on an external body region proximate an artery
Data Source
AI summary
Non-invasive methods for detecting arterial disease in vivo include obtaining acoustic signals from a sensor held on an external body region proximate an artery. A complex frequency grid of frequencies and associated lifetimes of the obtained acoustic signals is generated. A predictive model of complex frequencies associated with peak-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with early stage arterial disease is provided. A predictive model of complex frequencies associated with line-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with later stage arterial disease and thickening of arterial junctions is also provided. It is determined whether peak and/or line-perturbation acoustic signals of the predictive models are present to detect whether the subject has arterial disease.