ECG Pace Pulse Detection With Dynamic Noise Thresholding
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Solution Overview
Problem
Existing pacemaker detection systems struggle with low sensitivity and specificity in identifying pace pulses due to their small amplitude and narrow width, leading to false-negative and false-positive rates, especially with advanced pacemakers like leadless and His bundle pacing.
Innovation Solution
A method and apparatus for pace pulse detection using multiple ECG leads, employing dynamic threshold generation based on multi-level noise measurement and real-time noise updates, combined with morphological analysis and cross-lead validation to identify and validate pace pulses accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fixed threshold detection is used for pace pulse detection, then the detection system is simple, but the sensitivity and specificity are low due to inability to adapt to varying noise levels and pacemaker types
Solution Approach 1:
The patent implements dynamic threshold generation that automatically adapts to varying noise levels and pacemaker types in real-time. The system calculates thresholds based on current signal characteristics rather than using fixed predetermined values, enabling the detector to maintain optimal sensitivity and specificity across different clinical scenarios without manual reconfiguration.
Solution Approach 2:
The detection system incorporates feedback mechanisms where the measured noise levels and signal characteristics are continuously fed back into the threshold generation algorithm. This feedback loop allows the system to learn from each signal and adjust thresholds accordingly, improving detection accuracy over time while adapting to individual patient physiology and pacemaker variations.
2Measurement precision
If detection threshold is set low to capture small amplitude pace pulses, then sensitivity increases, but false-positive rate increases due to noise misinterpretation
Solution Approach 1:
The system dynamically changes detection parameters including threshold values, filtering characteristics, and analysis windows based on real-time noise measurement. By adjusting these parameters adaptively, the system maintains low false-positive rates while capturing small amplitude pulses, preventing both missed detections and spurious alarms through intelligent parameter modulation.
Solution Approach 2:
The system performs preliminary noise measurement and threshold calculation before actual pace pulse detection begins. This preliminary action establishes an optimized detection framework in advance, ensuring that when small amplitude pulses occur, the system is already configured with appropriate sensitivity settings, reducing both false negatives and false positives from the outset.
3Reliability
If detection threshold is set high to reduce false-positive rate, then specificity improves, but false-negative rate increases for small amplitude pulses
Solution Approach 1:
Rather than using a static high threshold that sacrifices sensitivity, the system dynamically adjusts thresholds based on real-time noise characterization. When noise levels are low, the system uses higher thresholds to maintain specificity; when noise increases or pulse amplitude decreases, the threshold automatically lowers to preserve sensitivity, achieving both high specificity and sensitivity across varying conditions.
4Measurement precision
If multi-level noise measurement and dynamic threshold generation are implemented, then detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The detection process is segmented into distinct functional stages: noise measurement phase, threshold generation phase, and pulse detection phase. Each stage operates independently with defined inputs and outputs, allowing optimized processing for each function. This segmentation reduces overall computational burden by avoiding unnecessary processing at all stages and enables parallel execution of independent tasks.
Solution Approach 2:
The system performs partial noise measurement and threshold calculation only when necessary, such as during idle periods or when signal characteristics change significantly. Rather than continuously processing at maximum complexity, the system applies computational resources selectively, achieving high detection accuracy when needed while minimizing overall processing load during stable conditions.
Data Source
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AI summary
An apparatus and method for detecting a pace pulse signal are described. The apparatus and the method include receiving a plurality of sample signals from one ECG lead, measuring a first-level noise of the plurality of sample signals during a first time interval, measuring a second-level noise of the plurality of sample during a second time interval, where the second time interval is different from the first time interval, generating one or more dynamic thresholds based on at least one of the measured first-level noise and the second-level noise, and detecting, based on the one or more dynamic thresholds, at least one of a start point, a peak point and an endpoint of the pace pulse signal from the plurality of sample signals.