Atrial Fibrillation Prediction via ECG Epoch Segmentation
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Solution Overview
Problem
Current methods fail to accurately predict the onset of atrial fibrillation (AF) episodes, which are intermittent and often undiagnosed, posing a risk due to their association with increased embolic stroke and mortality.
Innovation Solution
A system and method that analyzes historical ECG data to distinguish between distant-AF and pre-AF epochs, establishing a baseline to predict AF onset by monitoring current heart activity using an integrated vectorcardiogram device, employing machine learning techniques like neural networks and support vector machines to identify distinguishing features in RR-interval time series.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ECG monitoring methods are used to detect AF episodes, then the system is simple to operate, but the prediction accuracy of AF onset is insufficient
Solution Approach 1:
The patent segments the ECG signal analysis into distinct phases: identifying distant-AF epochs, identifying pre-AF epochs, and establishing baseline characteristics. This segmentation allows the system to focus computational resources on specific critical periods, improving prediction accuracy while managing complexity through structured analysis
Solution Approach 2:
The system performs preliminary identification of distant-AF epochs and pre-AF epochs before the actual AF onset. By establishing baseline characteristics during these preliminary phases and detecting deviations from the baseline, the system achieves early prediction of AF onset, improving measurement precision through advance detection
2Measurement precision
If historical ECG data is analyzed in detail to distinguish distant-AF and pre-AF epochs, then prediction accuracy improves, but processing time increases
Solution Approach 1:
The patent applies local quality analysis by focusing detailed examination only on specific epochs (distant-AF and pre-AF) rather than uniformly analyzing all ECG data. This targeted approach extracts distinguishing features from critical periods, achieving high classification accuracy (95.2%) while reducing overall processing time by avoiding unnecessary analysis of non-critical segments
3Reliability
If baseline characteristics are established using both pre-AF and distant-AF epochs, then prediction reliability improves, but data processing complexity increases
Solution Approach 1:
The system segments baseline establishment into two distinct components: pre-AF epoch characteristics and distant-AF epoch characteristics. By maintaining separate feature sets for each epoch type and comparing current activity against these segmented baselines, the system improves prediction reliability through comprehensive baseline coverage while managing processing complexity through structured organization
Solution Approach 2:
The baseline characteristics serve as an intermediary between historical ECG data and current real-time monitoring. By establishing reference baselines from historical data and comparing current electrical activity against these intermediaries, the system achieves reliable prediction without directly processing all historical data in real-time, thus managing complexity
Data Source
AI summary
System and method for providing patient-specific models to distinguish between epochs of electrocardiograms (ECGs) located far away from atrial fibrillation rhythms and those located just prior to the onset of those episodes, to provide for the prediction of the onset of an occurrence of atrial fibrillation (AF) in the patient.