Pacer Spike Detection in Twelve Lead ECGs
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Solution Overview
Problem
Conventional methods fail to accurately detect paced rhythms in twelve lead ECGs due to noise interference, making it difficult to distinguish pacer spikes from noise, especially as their amplitude varies across leads and can be masked by electrode contact noise, power-line interference, and muscle noise.
Innovation Solution
A system and method that involves passing ECG lead signals through a high-pass filter to identify pacer spikes, setting threshold limits to distinguish them from noise, pruning clusters to retain only the spike with the largest amplitude per beat, and eliminating spikes in noisy sections to enhance detection accuracy, followed by post-processing to confirm the paced rhythm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional methods combine pacer spikes across every lead to produce a final list, then the detection process is simplified, but the accuracy of paced rhythm detection deteriorates due to noise interference
Solution Approach 1:
The patent applies preliminary action by performing high-pass filtering and noise reduction on ECG signals before combining pacer spikes from multiple leads. This preprocessing step removes noise components that would otherwise interfere with accurate detection, allowing the subsequent combination of pacer spikes to produce more reliable results. The filtering operation is performed in advance to prepare the signals for accurate spike identification.
Solution Approach 2:
The patent segments the detection process into distinct stages: first filtering individual lead signals separately, then identifying pacer spikes in each filtered signal, and finally combining the identified spikes across leads. This segmentation allows noise to be removed at the source before combination, rather than attempting to separate signal from noise after combination, thereby improving detection accuracy while maintaining operational feasibility.
2Measurement precision
If threshold limits are set to identify pacer spikes, then the distinction between pacer spikes and noise is improved, but false detection increases due to varying amplitudes across different leads
Solution Approach 1:
The patent applies local quality by setting different threshold limits for different ECG leads based on their individual characteristics. Instead of using a uniform threshold across all leads, the system analyzes the signal properties of each lead and applies appropriately adjusted thresholds, allowing accurate spike detection in each lead's specific noise environment while maintaining overall detection reliability.
Solution Approach 2:
The patent changes the threshold parameter dynamically based on the characteristics of each lead's signal. By adjusting the threshold level according to the amplitude variations and noise levels specific to each lead, the system optimizes the balance between detecting true pacer spikes and avoiding false positives, thereby improving both distinction accuracy and detection reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively identifies pacer spikes and determines paced rhythms by filtering out noise, ensuring accurate detection and reducing errors, thereby improving the reliability of ECG analysis in patients with pacemakers.
Implementation Method 1
A signal from an ECG lead is passed through a high-pass filter
Data Source
AI summary
The invention provides a system and method for detecting a paced rhythm in a twelve lead ECG. A high-pass filter receives a signal from an ECG lead and pacer spikes within the high-pass filtered signal are distinguished from noise by setting certain threshold limits. Subsequently, clusters of pacer spikes are detected and raw pacer spikes corresponding to each cluster are determined. A pruning process is performed to identify one pacer spike corresponding to each paced beat and raw pacer spike with largest amplitude within a cluster is retained and other pacer spikes within the cluster are eliminated. Further, potential pacer spikes in the ECG lead are determined by eliminating pacer spikes in sections with missing data or high amount of noise. Further post-processing steps are performed to declare the identification of the paced rhythm in the ECG lead.