3D PPG Attractor Reconstruction for Interpretable Health Analysis
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Solution Overview
Problem
Wearable devices struggle with unintuitive time-domain PPG data interpretation, difficulty in feature identification, and high computational demands for deriving health-related insights, leading to inefficient physiological data analysis.
Innovation Solution
Convert PPG signals to Symmetric Projection Attractor Reconstruction (SPAR) representations in 2D/3D space for intuitive visualization and use machine learning to identify morphological features, adjusting operational parameters for improved data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If time-domain PPG data is used for analysis, then the data can be collected and processed, but the interpretation becomes unintuitive and feature identification becomes difficult
Solution Approach 1:
The patent transforms PPG data from time-domain representation to attractor reconstruction in phase space (typically 2D or 3D visualizations). This dimensional transformation converts complex temporal signals into geometric patterns that are visually interpretable while preserving all original information, thereby resolving the contradiction between ease of interpretation and information retention.
2Productivity
If traditional methods are used to derive health-related insights from PPG data, then analysis can be performed, but computational demands are high and efficiency is low
Solution Approach 1:
The patent extracts key morphological features directly from the geometric patterns of attractor reconstructions (such as loop area, shape characteristics, and spatial distribution) rather than processing entire time-domain signals. This extraction approach significantly reduces computational requirements while maintaining analysis efficiency, as the essential physiological information is captured in the simplified geometric representation.
3Measurement precision
If machine learning is used to identify morphological features in SPAR representations, then health-related insights become more accurate, but the system complexity increases
Solution Approach 1:
The patent introduces attractor reconstruction as an intermediary representation between raw PPG data and machine learning analysis. This intermediary geometric form simplifies the input data structure, making it more suitable for machine learning algorithms to identify morphological features accurately. The intermediary representation reduces the complexity burden on the overall system while improving measurement precision.
Data Source
AI summary
Methods, systems, and devices for photoplethysmogram (PPG) analysis are described. Techniques described herein may enable a system to convert a PPG signal from a time-domain signal into an attractor reconstruction representation in three-dimensional (3D) space. Visualizations of such 3D attractor reconstruction projections may be displayed to a user via a smart device. Further, the system may use machine learning models to identify morphological features within 3D attractor reconstruction projections to perform physiological measurements and determine health-related insights for the user. For example, a smart device may input time-domain PPG signals and 3D attractor reconstruction projections into machine learning models that are configured to identify morphological features within the time-domain PPG signals and attractor reconstruction projections. The smart device may perform various physiological measurements, such as cardiovascular age, blood pressure, heart rate variability, and the like.