Atrial Flutter Detection via Nonlinear Manifold Embedding
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Solution Overview
Problem
Conventional implantable cardiac monitors and devices struggle to accurately detect atrial flutter (AFL) due to its varying morphology and low amplitude relative to R-waves and T-waves, often failing to log AFL episodes when ventricular rates are below programmed tachycardia thresholds, and relying on R-R interval variability which can lead to missed detections.
Innovation Solution
A computer-implemented method utilizing nonlinear dimension reduction techniques, such as locally linear embedding, to map high-dimensional cardiac activity data into a lower-dimensional space, allowing for the detection and discrimination of AFL episodes based on specific classification criteria, and configuring implantable devices with manifold structures for real-time AFL detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AF detection algorithms are used, then atrial fibrillation can be detected, but atrial flutter detection accuracy deteriorates due to morphology variation and low amplitude
Solution Approach 1:
The patent transforms the cardiac signal detection problem from traditional time-domain analysis to a manifold-based geometric space. By embedding high-dimensional cardiac activity data into a lower-dimensional manifold structure, the system creates a new dimensional framework where AFL patterns can be distinguished from AF and normal rhythms based on their geometric characteristics rather than amplitude or morphology alone.
Solution Approach 2:
The patent changes the detection parameters from conventional amplitude-based and morphology-based features to manifold distance metrics and geometric pattern recognition. This parameter transformation allows the system to detect AFL episodes by measuring distances and relationships in the manifold space, making the detection insensitive to amplitude variations and morphology changes that plague conventional algorithms.
2Measurement precision
If tachycardia threshold programming is used, then high ventricular rate events are detected, but low ventricular rate AFL episodes are missed
Solution Approach 1:
The manifold-based detection system serves multiple functions simultaneously: it detects AFL episodes across all ventricular rate ranges, distinguishes AFL from AF, and identifies the characteristic 2:1, 3:1, and higher conduction patterns. This universal approach replaces the need for separate detection algorithms tailored to different rate thresholds, making the system adaptable to all ventricular rate conditions.
3Device complexity
If R-R interval variability analysis is used, then arrhythmia detection is simplified, but AFL detection fails due to regular R-R intervals
Solution Approach 1:
The patent introduces a manifold structure as an intermediary between the raw cardiac signal and the detection decision. Instead of directly analyzing R-R interval variability or signal amplitude, the system first transforms the data into a manifold space where the inherent geometric patterns of AFL emerge. This intermediary transformation layer enables accurate AFL detection without requiring complex direct analysis of the original signal features.
4Measurement precision
If high-dimensional feature analysis is used, then detection accuracy improves, but computational burden increases
Solution Approach 1:
The patent applies dimensionality reduction by embedding high-dimensional cardiac activity data into a lower-dimensional manifold structure. This dimensional transformation preserves the essential discriminatory information needed for AFL detection while reducing the computational burden. The manifold space maintains the geometric relationships necessary for accurate classification but requires less computational resources to process and analyze.
Data Source
AI summary
A computer implemented method and system for declaring arrhythmias in cardiac activity are provided. The method and system are under control of one or more processors that are configured with specific executable instructions. The method and system obtain far field cardiac activity (CA) signals for a series of beats and builds an N-dimensional data set from data values for features of interest from the CA signals. The method and system utilize a manifold structure to map the N-dimensional data set, through nonlinear dimensional reduction, onto an M-dimensional data set and declares an atrial fibrillation (AFL) episode based on a relation between the M-dimensional data set and one or more AFL classification criteria.