PPG AFib Detection Using Flux-Interval Plot Irregularity Analysis
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Solution Overview
Problem
Existing atrial fibrillation detection methods, particularly those based on photoplethysmogram (PPG) signals, struggle to accurately distinguish AFib from other cardiac arrhythmias and lack transparency, making it difficult for users to interpret the results.
Innovation Solution
The system employs flux-interval plots and calculates the Interval Irregularity Index (III) and Regression RMSE of Main Cluster to analyze PPG signals, providing visual characterization and transparent decision-making through DBSCAN clustering and orthogonal regression, allowing users to understand the basis of the model's predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning approaches are used for PPG-based AFib detection, then detection accuracy is improved, but model complexity and difficulty of interpretation increase
Solution Approach 1:
The patent segments the PPG signal analysis into distinct components: pulse amplitude extraction, interval calculation, flux-interval plot generation, and clustering analysis. This segmentation allows complex machine learning models to process information in manageable chunks while maintaining interpretability through visual plots and structured data representations.
Solution Approach 2:
The patent introduces flux-interval plots as an intermediary visual representation between raw PPG signals and detection decisions. These plots serve as a mediator that translates complex signal processing into intuitive visual patterns, enabling users to understand model reasoning without requiring expertise in machine learning algorithms.
2Ease of operation
If statistical analysis approaches are used for PPG-based AFib detection, then ease of operation is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent merges the simplicity of statistical analysis with the power of machine learning by combining threshold-based detection rules with clustering algorithms and flux-interval plot visualization. This hybrid approach maintains ease of operation through intuitive visual interfaces while achieving high accuracy through sophisticated underlying algorithms.
3Measurement precision
If deep learning approaches with automatic feature extraction are used, then detection accuracy is improved, but loss of information about model decision basis increases
Solution Approach 1:
The patent implements feedback mechanisms by generating flux-interval plots that show the relationship between pulse flux and interval for each detected anomaly. These visual feedback plots provide transparent insight into model decision-making processes, allowing users to understand why a particular classification was made while maintaining the accuracy benefits of deep learning.
4Ease of operation
If PPG signals are used instead of ECG, then ease of operation and affordability are improved, but measurement precision for AFib detection deteriorates
Solution Approach 1:
The patent transforms the one-dimensional PPG amplitude signal into two-dimensional flux-interval plots that visualize the relationship between pulse flux and interval. This dimensional transformation reveals patterns and relationships in the data that are not apparent in raw PPG signals alone, enabling accurate AFib detection while maintaining the ease of use and affordability of PPG technology.
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 achieves high accuracy in differentiating AFib from other cardiac arrhythmias with minimal false positives and negatives, offering transparent and user-friendly results.
Implementation Method 1
PPG signal also originates from the cardiac cycle which inherits the ability to extract the same variability features but is more circumstantial as it is the measurement of blood flow volume difference in the capillary caused by each heartbeat
Data Source
AI summary
The present invention provides an atrial fibrillation detection system and method based upon irregularity in inter-pulse duration or interval of a subject's cardiovascular signal. Specifically, the present invention determines existence of atrial fibrillation based upon irregularity in percentage difference in duration or interval of consecutive pulses of a subject's cardiovascular signal. In addition, the present invention applies analysis of flux-interval plots of pulse interval and pulse normalized amplitude to screen out false positives and displays the flux-interval plots to the subject to provide transparency to the subject.