Cardiac Wave Onset Detection via Slope Thresholding
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Solution Overview
Problem
Current medical devices for cardiac electrogram analysis face challenges in accurately determining the onsets and offsets of heart depolarization and repolarization waves, which hinders the precise calculation of diagnostic metrics and optimal configuration of cardiac resynchronization therapy (CRT).
Innovation Solution
A system comprising a cardiac electrogram module, peak detection module, wave detection module, and motion sensing module, which uses low-pass filtering, slope determination, rectification, smoothing, and threshold detection to accurately identify the onsets and offsets of cardiac waves, enabling the calculation of diagnostic metrics like QRS width and electromechanical delay for guiding CRT configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple threshold-based detection is used for wave onset/offset detection, then the device complexity is reduced, but the measurement precision of cardiac wave timings deteriorates
Solution Approach 1:
The detection process is segmented into multiple distinct stages: preprocessing (filtering, rectification, smoothing), slope calculation, peak detection, and threshold-based onset/offset determination. This segmentation allows each stage to be optimized independently, achieving high measurement precision through coordinated simple operations rather than a single complex algorithm.
Solution Approach 2:
Preliminary signal processing operations (low-pass filtering, rectification, smoothing) are performed before the actual wave detection. These preliminary actions prepare the signal by removing noise and enhancing relevant features, enabling subsequent simple threshold-based detection to achieve high precision without requiring complex algorithms.
2Measurement precision
If multiple signal processing steps (filtering, rectification, smoothing) are applied, then the measurement precision of wave timings is improved, but the device complexity increases
Solution Approach 1:
Intermediate processed signals are generated at each processing stage (filtered signal, rectified signal, smoothed signal) to serve as inputs for the next stage. These intermediary representations progressively transform the raw signal into a form that is optimized for specific detection tasks, enabling high precision measurement through a sequence of simple, well-defined operations.
3Productivity
If automated wave detection algorithms are used, then the productivity of diagnostic metric calculation is improved, but the measurement precision may deteriorate due to algorithmic approximations
Solution Approach 1:
The system performs self-service automated detection using objectively defined mathematical criteria (slope thresholds, peak detection rules, fixed percentage thresholds) rather than requiring manual interpretation. This automation maintains high productivity while ensuring measurement precision through consistent, reproducible algorithms that eliminate human variability and subjective judgment.
Data Source
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AI summary
A method for detecting the onsets or offsets of heart depolarization or repolarization waves (e.g. QRS-complex or T-wave) includes determining a slope of a cardiac electrogram. A threshold value is determined based on a maximum of the slope of the cardiac electrogram, and a last point of the cardiac electrogram before the slope of the cardiac electrogram crosses the threshold is identified as one of an onset or an offset of a wave. The method may further include receiving an indication of local ventricular motion associated with a cardiac contraction, and determining an electromechanical delay between the identified onset and the local ventricular motion. Some examples include providing the electromechanical delay for configuration of cardiac resynchronization therapy (CRT), e.g. selection of pacing sites.