Cardiac Pacing Effectiveness Tracking via Morphological Feature Analysis
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Solution Overview
Problem
Existing methods for monitoring pacing effectiveness in cardiac resynchronization therapy (CRT) are computationally intensive and unreliable due to pacing artifacts in electrogram waveforms, making it challenging to accurately classify rhythms and assess therapy efficacy.
Innovation Solution
A feature-based algorithm that analyzes gross morphological features of ventricular electrogram waveforms during CRT pacing, comparing them to stored reference features to classify paced events as effective or ineffective, and tracks a pacing effectiveness ratio to monitor therapy efficacy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template matching algorithms are used for morphology analysis, then rhythm classification accuracy may be improved, but computational complexity increases making it impractical for IMD execution
Solution Approach 1:
The patent segments the complex template matching process into simpler feature extraction steps. Instead of comparing entire waveforms, the system extracts specific features (peak-to-peak amplitude, peak slope, peak number, timing relationships) and compares these individual characteristics. This segmentation reduces computational complexity while maintaining classification accuracy.
Solution Approach 2:
The patent extracts key morphological features from the electrogram waveforms, taking out only the essential characteristics needed for rhythm classification. By focusing on specific features like amplitude, slope, and timing rather than the complete waveform, the system achieves accurate classification with reduced computational burden suitable for IMD execution.
2Measurement precision
If detailed template matching methods are used, then morphology analysis precision may be improved, but reliability decreases due to pacing artifacts corrupting EGM waveforms
Solution Approach 1:
The patent converts the harmful effect of pacing artifacts into a beneficial feature. Rather than attempting to eliminate or avoid artifacts, the system uses them as part of the classification process by comparing waveforms during and after pacing. The artifacts provide consistent reference points that, when incorporated into the feature extraction, actually improve the reliability of rhythm classification in paced conditions.
3Productivity
If beat-to-beat rhythm classification is implemented, then pacing effectiveness monitoring is improved, but computational load increases for real-time processing
Solution Approach 1:
The patent applies partial action by performing classification on a selected subset of beats rather than every single beat. The system identifies and classifies representative beats that provide sufficient information about pacing effectiveness, reducing the overall computational load while maintaining accurate monitoring. This selective approach allows beat-to-beat monitoring capability without the full processing burden of analyzing every waveform.
Data Source
AI summary
Methods and/or devices may be configured to track effectiveness of pacing therapy by monitoring two or more electrical vectors of the patient's heart during pacing therapy and analyzing at least one feature of a morphological waveform within each of the two or more electrical vectors.