Phase-Space Point Cloud for Non-Invasive Disease Diagnosis
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Solution Overview
Problem
Current methods for diagnosing coronary artery disease and other cardiovascular and neurological conditions are invasive, unreliable, and struggle to accurately capture complex nonlinear variability in biophysical signals, leading to challenges in precise diagnosis and treatment.
Innovation Solution
A non-invasive system that acquires biophysical signals to generate a point-cloud residue data set, which is structured into a three-dimensional volumetric object, allowing for the extraction of machine-readable features for disease prediction and classification using machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional modeling techniques are used to capture cardiac phase gradient signals, then the measurement process is simple, but the complex nonlinear variability cannot be efficiently captured leading to reduced measurement precision
Solution Approach 1:
The patent transforms the cardiac phase gradient signal from time-domain to phase-space by creating a point cloud representation where each point corresponds to a phase angle and amplitude pair. This parameter transformation enables efficient capture of nonlinear variability that traditional time-series modeling cannot represent, directly resolving the contradiction between measurement precision and modeling complexity.
Solution Approach 2:
The invention adds a dimensional transformation by mapping the one-dimensional time-series signal into a two-dimensional phase-space point cloud, where the x-axis represents phase angle and the y-axis represents amplitude. This dimensional change allows the system to capture complex nonlinear patterns and circadian rhythms that are invisible in traditional time-domain analysis, achieving higher measurement precision without proportionally increasing complexity.
2Reliability
If invasive procedures are used for disease diagnosis, then diagnostic accuracy may be improved, but patient harm and procedure complexity increase
Solution Approach 1:
The patent replaces invasive mechanical measurement systems with non-invasive optical or electromagnetic sensing that captures cardiac phase gradient signals through the body surface. This substitution eliminates the harmful effects of invasive procedures while maintaining diagnostic reliability by using advanced signal processing (phase-space transformation and machine learning) to extract meaningful information from the non-invasive measurements.
Solution Approach 2:
The invention introduces an intermediary computational layer (machine learning model trained on phase-space point clouds) that bridges the gap between non-invasive surface measurements and internal disease states. This intermediary processes the complex nonlinear patterns in the signals to achieve diagnostic accuracy comparable to or exceeding invasive methods, while avoiding the harms of invasion.
3Measurement precision
If detailed biophysical signal analysis is performed to improve diagnosis, then measurement precision increases, but data processing complexity and time increase
Solution Approach 1:
The patent performs preliminary transformation of the raw cardiac signals into phase-space point cloud representations during the data acquisition phase. This preliminary action organizes the data into a structured format that highlights key diagnostic features, reducing the computational burden during actual diagnosis. The phase-space transformation is computationally efficient compared to traditional time-series analysis methods, thereby reducing processing time while maintaining or improving diagnostic precision.
Solution Approach 2:
The invention extracts only the essential diagnostic features by transforming the full-time-series signal into a condensed phase-space point cloud representation. This extraction process removes redundant information and focuses on the critical nonlinear patterns and circadian rhythms that are most relevant for diagnosis, thereby reducing data processing complexity and time while preserving measurement precision.
Data Source
AI summary
The exemplified methods and systems employs non-invasively acquired biophysical measurements of a subject in a residue analysis that is structured as a three-dimensional volumetric object to which machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object may be derived and used for in the training and/or prediction of a disease state. The system generates a residue model from a point-cloud residue generated from an analysis of the plurality of biophysical signal data sets. The system generates a three-dimensional volumetric object from the point-cloud residue from which machine extractable features associated with the point-cloud residue maybe extracted.