Dual-Processing AFIB Detection System Using Beat and Segment Modules
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Solution Overview
Problem
Current systems for automatically detecting atrial fibrillation (AFIB) in ECG data face challenges with unreliable beat-by-beat analysis, especially in short recordings and noisy environments, and require multiple leads and clean data, making them unsuitable for long-term continuous monitoring.
Innovation Solution
A dual-processing system that combines a beat module using Hidden Markov Models for beat-by-beat analysis and a segment module analyzing overlapping time segments, allowing for robust AFIB detection regardless of signal source, lead arrangement, or noise level, by comparing results from both modules to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If beat-by-beat analysis is used for AFIB detection, then detection speed is improved, but reliability deteriorates in noisy environments and short recordings
Solution Approach 1:
The patent divides the ECG signal into multiple time segments and applies different analysis methods to each segment. The beat-by-beat analysis is performed on individual segments rather than the entire recording, allowing for faster processing while maintaining reliability through multiple independent analyses that can be combined.
Solution Approach 2:
The patent introduces a temporal dimension by analyzing multiple time segments and combining results. Instead of relying on a single beat-by-beat analysis, the system performs analyses across different time windows and aggregates the findings, adding a dimensional layer that improves reliability without sacrificing detection speed.
2Measurement precision
If multiple leads are required for accurate AFIB detection, then detection accuracy is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The patent creates a detection system that is universal across different lead configurations. The algorithm can process and detect AFIB accurately whether single-lead or multi-lead ECG data is provided, eliminating the need for separate systems for different lead arrangements and reducing overall device complexity.
Solution Approach 2:
The patent adapts analysis parameters based on the available lead configuration. When fewer leads are available, the system adjusts its detection thresholds and analysis methods to maintain accuracy, allowing flexible operation across different complexity levels without sacrificing detection precision.
3Measurement precision
If clean ECG data is required for reliable detection, then measurement precision is improved, but adaptability worsens in noisy environments
Solution Approach 1:
The patent implements dynamic noise tolerance that adapts to the quality of the input signal. The system automatically adjusts its detection criteria and analysis depth based on the observed noise level, maintaining measurement precision whether the ECG data is clean or noisy without requiring manual intervention.
Solution Approach 2:
The detection system performs self-adjustment by automatically assessing signal quality and modifying its analysis parameters accordingly. The algorithm identifies and compensates for noise characteristics in the ECG data, enabling reliable detection across varying environmental conditions without external calibration or intervention.
4Measurement precision
If extensive manual processing is performed on ECG recordings, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual mechanical review of ECG recordings with an automated computational system. The algorithm performs complex pattern recognition and detection tasks that previously required human expertise, maintaining high measurement precision while dramatically increasing processing throughput and eliminating manual labor bottlenecks.
Data Source
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AI summary
A system for processing ECG to detect atrial fibrillation includes three software modules. A beat module is executable on a processor to receive a time series of ECG data, identify heart beats, and determine a beat AFIB value based on a timing of each identified heart beat. The beat AFIB value represents a presence or absence of AFIB based on variability in the timing of each identified heart beat. A segment module is executable to receive the time series of ECG data, divide the time series of ECG data into two or more time segments, and determine a segment AFIB value for each time segment. The segment AFIB value indicates a presence or absence of AFIB in the time segment based on whether any of a set of rhythms are identified. The AFIB detection module is executable to determine an AFIB identification value for each time segment based on the beat AFIB value during that time segment and the segment AFIB value for that time segment.