Conduction Deviation Features for Non-Invasive Cardiac Diagnosis
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Solution Overview
Problem
Current methods for diagnosing cardiac diseases and conditions, such as pulmonary hypertension and coronary artery disease, often require invasive procedures, specialized facilities, or radiation, posing risks and disadvantages.
Innovation Solution
A clinical evaluation system using conduction deviation features derived from biophysical signals, processed through a Multi-Dimensional Fourier Decomposer (MDFD), to estimate disease presence, severity, and localization via machine-learned classifiers, without invasive techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive procedures such as cardiac catheterization are used to diagnose cardiac diseases, then measurement precision is improved, but object-affected harmful factors increase due to procedural risks
Solution Approach 1:
The patent replaces invasive mechanical procedures (cardiac catheterization) with non-invasive biophysical signal analysis. The system uses electrical signals naturally produced by the heart (ECG, ballistocardiogram) and processes them through computational algorithms to diagnose cardiac conditions, eliminating the need for physical catheter insertion while maintaining diagnostic capability
Solution Approach 2:
The patent introduces biophysical signal processing as an intermediary between the heart's electrical activity and clinical diagnosis. Instead of directly measuring pressure through invasive catheters, the system uses intermediate biophysical signals (electrical, mechanical, optical) that can be measured non-invasively and computationally transformed into diagnostic information
2Measurement precision
If specialized facilities such as MRI or CT scanners are used for cardiac imaging, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates computational models that replicate the diagnostic information obtained from complex imaging systems. Instead of requiring actual MRI or CT scanners, the system uses machine learning models trained on imaging data to generate equivalent diagnostic insights from simpler, non-invasive biophysical measurements
Solution Approach 2:
The patent transforms the measurement parameters from complex imaging modalities to simpler biophysical signals. By changing from measuring anatomical structures directly (requiring MRI/CT) to measuring electrical and mechanical parameters (accessible through standard sensors), the system achieves similar diagnostic value with reduced complexity
3Reliability
If conventional diagnostic methods are used, then reliability is maintained, but loss of time increases due to procedural duration
Solution Approach 1:
The patent performs preliminary computational processing of biophysical signals in real-time, allowing for immediate diagnostic assessment. The machine learning models are pre-trained and ready to process incoming signal data without requiring lengthy post-processing or interpretation sessions, enabling rapid diagnosis while maintaining reliability through validated algorithms
Data Source
AI summary
A clinical evaluation system and method are disclosed that facilitate the use of one or more conduction deviation features or parameters determined from biophysical signals such as cardiac or biopotentials signals. Conduction derivation features or parameters may include VD conduction derivation features or parameters and/or VD conduction derivation Poincaré features or parameters. The conduction derivation features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, a medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.