Causality Score for Atrial Fibrillation Triggers
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Solution Overview
Problem
Current methods for managing atrial fibrillation (AF) rely heavily on pharmacological treatments, which come with side effects and high costs, and lack effective nonpharmacological strategies for early-stage self-management, particularly in establishing causality between AF triggers and temporal episode patterns.
Innovation Solution
A method utilizing distributed software modules on personal computing devices and cloud servers to calculate a causality score between reported and detected AF triggers and temporal AF episode patterns, using biosignals from wearable devices to identify triggers and characterize episode patterns, thereby informing lifestyle changes for nonpharmacological management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pharmacological treatments are used for AF management, then treatment effectiveness is improved, but side effects and costs increase
Solution Approach 1:
The system enables patients to self-manage AF by identifying personal triggers through biosignal analysis and providing personalized recommendations. Patients actively participate in monitoring their own heart rhythm, triggers, and lifestyle factors, transforming from passive recipients of pharmacological treatment to active managers of their condition.
Solution Approach 2:
The system continuously monitors biosignals and provides real-time feedback to patients about AF episodes, trigger associations, and causal relationships. This feedback loop enables patients to adjust their behavior based on objective data about what triggers their AF, replacing pharmacological intervention with behaviorally-driven management.
2Measurement precision
If continuous AF monitoring is implemented, then detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The system leverages existing wearable devices (smartwatches, fitness trackers) that already perform multiple functions including step counting, sleep monitoring, and heart rate tracking. By adding AF detection capability to these universal devices, the system avoids the need for dedicated complex monitoring equipment while maintaining continuous monitoring accuracy.
Solution Approach 2:
The system uses optical PPG signals captured by standard wearable sensors as copies or representations of actual cardiac electrical activity. Instead of requiring direct electrical sensing from the heart, the system infers AF presence from peripheral blood flow patterns, simplifying the hardware requirements while maintaining detection capability.
3Manufacturing precision
If trigger identification and causality analysis are performed, then management precision is improved, but computational complexity increases
Solution Approach 1:
The system divides the complex causality analysis into separate modular components: trigger detection module, AF episode detection module, temporal pattern analysis module, and causality assessment module. Each module processes specific data independently before integrating results, reducing the computational burden on any single component while maintaining overall management precision.
Data Source
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AI summary
Method for establishing a causality score between atrial fibrillation triggers and atrial fibrillation occurrence pattern for use for management of personally identified atrial fibrillation triggers in relation to temporal atrial fibrillation occurrence patterns, the method steps are realized in the software modules distributed between personal computing device and cloud-based server wherein the method steps comprise detecting (101) time-specific AF trigger occurrence instance in biosignals, enrolling (102) data representing reported AF triggers, transforming (103) the enrolled data representing AF triggers to time-series signals, constructing (104) temporal AF episode occurrence patterns, characterizing (105) temporal AF episode occurrence patterns with respect to episode aggregation or/and clustering, matching (106) AF trigger and temporal AF episode occurrence pattern data, calculating (107) the causality score between the suspected AF triggers and the temporal AF episode occurrence pattern for identifying personal AF triggers.