Cardiac Rhythm Discrimination via Tachy-Brady Transition Detection
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Solution Overview
Problem
Existing algorithms for detecting atrial fibrillation (AF) in implantable cardiac monitors often result in false positives due to the difficulty in detecting P-waves, leading to incorrect identification of AF episodes, and lack sufficient positive predictive value for episode detection and duration.
Innovation Solution
A computer-implemented method and system that discriminates rhythm patterns in cardiac activity by calculating cardiac beats timing relations, identifying transitions between fast and slow irregular rhythm patterns, and recording Tachy-Brady episodes, which are then displayed and quantified as Tachy-Brady burden, using a processor-based system with memory and electrodes for sensing cardiac activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing AF detection algorithms are used based on irregularity of R-waves, then the system can detect potential AF episodes, but the system produces false positives and lacks sufficient positive predictive value
Solution Approach 1:
The patent segments the detection process into multiple stages: initial AF detection based on R-wave irregularity, followed by segmentation of the episode into multi-beat segments, and further segmentation into fast and slow segments based on rate thresholds. This multi-level segmentation allows the system to analyze different characteristics of the arrhythmia episode separately, improving the ability to distinguish true AF from false positives while maintaining high sensitivity for initial detection.
2Device complexity
If the system uses only 2 electrodes for monitoring, then the device complexity is reduced, but the system cannot effectively detect P-waves leading to insufficient discrimination capability
Solution Approach 1:
The patent introduces an intermediary computational approach that processes ventricular electrogram signals to extract P-wave information indirectly. By analyzing the timing relationships between P-waves and QRS complexes, and using algorithms to detect P-wave absence or abnormal characteristics, the system achieves P-wave detection capability without requiring additional electrodes. This intermediary method bridges the gap between limited hardware and enhanced diagnostic capability.
3Speed
If existing algorithms declare AF detection without P-wave verification, then the detection speed is maintained, but the diagnostic accuracy decreases due to inability to observe sinus beats or aberrations
Solution Approach 1:
The patent performs preliminary actions by continuously monitoring and characterizing the arrhythmia episode in real-time. The system pre-segments the episode into multi-beat segments and further into fast and slow segments, establishing a detailed temporal structure before making the final AF versus non-AF determination. This preliminary segmentation and characterization provide a comprehensive basis for accurate discrimination while maintaining detection speed, as the structural analysis is performed concurrently with the detection process.
Data Source
AI summary
Methods and systems are provided for discriminating rhythm patterns in cardiac activity. The method and system obtain cardiac activity data for multiple cardiac beats over a predetermined period of time. Multi-beat segments within the cardiac activity data exhibit different rhythm patterns of interest including fast and slow rhythm patterns. The method and system calculate a cardiac beats timing relation representative of intervals between the cardiac beats within a measurement window, wherein the measurement window is configured to overlap the corresponding multi-beat segment. The method and system designate the cardiac beats timing relation to have one of the rhythm patterns of interest based on a rate threshold, identifies when successive multi-beat segments exhibit rhythm patterns that transition between the fast and slow irregular rhythm patterns and records the irregular rhythm pattern transition in connection with the cardiac activity data.