Atrial Fibrillation Detection via Interbeat Interval Scatter Plots
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Solution Overview
Problem
Current methods for detecting atrial fibrillation (AF) are inadequate for early and accurate identification, leading to delayed diagnosis and treatment, as they often rely on unpredictable symptoms and require prolonged ECG monitoring.
Innovation Solution
An atrial fibrillation detection system that processes biosignals from a beat detector, converts them into interbeat interval sequences, generates scatter plots and histograms, and uses a classifier to determine the probability of AF or non-AF rhythms, enabling prompt diagnosis and treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 7-day continuous ECG monitoring is used to detect AF, then detection coverage is improved (70% of AF patients documented), but loss of time and device complexity increase
Solution Approach 1:
The patent segments the continuous ECG signal into discrete beats and calculates interbeat intervals (IBIs). By dividing the monitoring task into individual beat analysis units, the system can process ECG data in manageable segments rather than requiring continuous 7-day monitoring, thus reducing the effective monitoring time needed while maintaining detection reliability.
Solution Approach 2:
The patent introduces scatter plots and histograms as intermediary representations of the ECG data. These visual intermediaries transform complex temporal ECG patterns into spatial distributions that can be analyzed more quickly by classifiers, reducing the time required for accurate AF detection without sacrificing reliability.
2Reliability
If traditional ECG monitoring methods are used, then detection coverage is improved, but measurement precision and early detection capability deteriorate
Solution Approach 1:
The patent transforms the temporal dimension of ECG signals into spatial dimensions by creating scatter plots where each point represents an interbeat interval relationship. This dimensional transformation allows classifiers to detect AF patterns more precisely by analyzing spatial distributions rather than temporal sequences, improving measurement precision for early AF identification.
Solution Approach 2:
The patent changes the analysis parameters from raw ECG waveforms to derived parameters including interbeat intervals, scatter plot coordinates, and histogram bin counts. These parameter transformations enhance the precision of AF detection by focusing on specific characteristics that differentiate AF from normal rhythms, enabling earlier and more accurate diagnosis.
3Productivity
If real-time processing of long-term ECG signals is performed, then productivity is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the essential features from long-term ECG signals for real-time processing: interbeat intervals, scatter plot representations, and histogram summaries. By taking out only these critical elements rather than processing the entire continuous signal, the system achieves real-time productivity while reducing computational complexity and device requirements.
Data Source
AI summary
An atrial fibrillation detection system and associated methods of use are disclosed for analyzing and identifying atrial fibrillation signs of a user. In at least one embodiment, the system includes an at least one computing device configured for receiving and processing an at least one biosignal associated with heart activity of the user as captured by an at least one beat detector, and subsequently transmitting said processed at least one biosignal, as a plurality of sequence segments, making up an at least one interbeat interval sequence of the biosignal, to an at least one classifier configured for determining a probability of the at least one interbeat interval sequence of the biosignal being an atrial fibrillation rhythm or a non-atrial fibrillation rhythm.