Neural Network Atrial Fibrillation Screening ECG Analysis
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Solution Overview
Problem
Current methods for detecting atrial fibrillation in patients with cryptogenic stroke are invasive, expensive, and require long-term monitoring, which is not efficient for identifying paroxysmal atrial fibrillation, a common cause of cryptogenic stroke.
Innovation Solution
A deep neural network is trained to process short ECG recordings to predict the likelihood of atrial fibrillation and other supraventricular tachycardias from normal sinus rhythm ECGs, allowing for quick screening without the need for continuous monitoring, using a 12-lead system or single-lead smartphone patch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If implantable loop recorder is used to detect atrial fibrillation, then detection reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical implantable loop recorder system with a neural network-based computational model that processes standard ECG signals. The neural network analyzes subtle patterns in routine ECG recordings to predict atrial fibrillation risk, eliminating the need for invasive implantable devices while maintaining high detection reliability through sophisticated algorithmic analysis.
Solution Approach 2:
The patent introduces a neural network as an intermediary between standard ECG recordings and atrial fibrillation detection. This intermediary model processes routine ECG data, identifies predictive patterns, and generates risk predictions, thereby bridging the gap between simple ECG monitoring and reliable AF detection without requiring complex implantable hardware.
2Reliability
If continuous monitoring infrastructure is implemented, then detection reliability is improved, but loss of time and cost increase
Solution Approach 1:
The patent applies preliminary action by using the neural network to analyze routine ECG recordings and predict future atrial fibrillation episodes before they occur. The model identifies subtle patterns in current ECG data that predict future AF events, allowing for proactive intervention without requiring continuous long-term monitoring infrastructure.
Solution Approach 2:
The patent uses partial action by analyzing only the essential features from routine ECG recordings rather than requiring continuous comprehensive monitoring. The neural network extracts and processes only the most predictive patterns from standard ECG data, achieving reliable detection without the time and resource commitment of continuous monitoring infrastructure.
3Reliability
If long-term monitoring is used to detect paroxysmal atrial fibrillation, then detection reliability is improved, but productivity decreases
Solution Approach 1:
The patent replaces lengthy mechanical monitoring processes with a neural network-based predictive model that rapidly analyzes routine ECG recordings. This substitution enables quick risk assessment and stratification of patients, dramatically improving screening efficiency while maintaining high detection reliability through sophisticated pattern recognition algorithms.
Solution Approach 2:
The patent changes the parameter of analysis from continuous time-based monitoring to pattern-based prediction. Instead of requiring long-term continuous monitoring to capture paroxysmal events, the neural network analyzes static ECG parameters and temporal patterns to predict future AF episodes, thereby improving productivity while maintaining detection reliability.
Data Source
AI summary
Systems, methods, devices, and other techniques for processing an ECG recording to assess a condition of a mammal. Assessing the condition of the mammal can include screening for atrial fibrillation, and screening for atrial fibrillation can include obtaining a first neural network input, the first neural network input representing an electrocardiogram (ECG) recording of the mammal, and processing the first neural network input with a neural network to generate an atrial fibrillation prediction for the mammal.