Cardiac Activation Detection Using Deflection Consistency
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Solution Overview
Problem
Current electrocardiography systems face challenges in accurately detecting cardiac activations, especially in heterogeneous or transient conditions, due to intermittent electrode-tissue contact or during atrial fibrillation, which affects the creation of useful products like local activation time maps and conduction velocity maps.
Innovation Solution
The system employs an electrocardiogram system that computes a set of candidate detection time points based on activation responses, such as dv/dt or wavelet transform responses, and characterizes them by deflection characteristics like cycle length, voltage, and conduction velocity, then validates these points for consistency to identify final detection time points, which are used to compute metrics like regular cycle length and conduction velocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrocardiography systems detect cardiac activations using standard ECG electrodes and traces, then the system can measure rate and rhythm of heartbeats, but the detection accuracy deteriorates in heterogeneous or transient conditions due to intermittent electrode-tissue contact or during atrial fibrillation
Solution Approach 1:
The patent segments the detection process into multiple stages: identifying candidate deflections, computing deflection characteristics for each candidate, grouping candidates by characteristic similarity, and selecting final detection time points from consistent groups. This segmentation allows the system to handle heterogeneous conditions by processing detections in discrete, manageable steps with validation at each stage.
Solution Approach 2:
The patent changes the detection parameters by computing multiple deflection characteristics (amplitude, width, slope, area) for each candidate deflection and using consistency of these parameters across multiple deflections as the basis for validation. This parameter-based approach enables the system to distinguish true cardiac activations from noise by requiring consistent parameter patterns rather than relying on single-threshold detection.
2Measurement precision
If the system computes multiple deflection characteristics for each candidate detection time point to validate consistency, then the detection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary computation of deflection characteristics for all candidate detection time points before grouping and validation. By pre-computing characteristics such as amplitude, width, slope, and area for each candidate, the system avoids repeated calculations during the grouping stage, reducing overall computational complexity despite the multiple characteristics being computed.
Solution Approach 2:
The patent creates simplified representations of deflection characteristics for each candidate detection time point, storing key parameters that capture the essential features without requiring the full original signal. This copying approach allows efficient comparison and grouping of candidates based on their characteristic similarity while reducing the computational burden of processing complete waveforms.
3Reliability
If the system groups candidate detection time points by deflection characteristic consistency, then the reliability of final detection time points improves, but the quantity of processing steps and data manipulation increases
Solution Approach 1:
The patent segments the candidate detection time points into groups based on similarity of their deflection characteristics. By dividing the candidates into manageable groups rather than processing all candidates individually or collectively, the system efficiently identifies consistent patterns while reducing the computational burden of comparing every candidate against every other candidate.
Solution Approach 2:
The patent transforms the detection problem by changing from direct signal analysis to analysis of derived parameters. By computing deflection characteristics (amplitude, width, slope, area) and grouping based on parameter consistency rather than raw signal similarity, the system achieves reliable detection with more efficient processing of transformed data.
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 enables accurate detection of cardiac activations, improves detection in non-homogeneous electrograms, distinguishes between local and nearby cardiac activity, and produces reliable metrics based on consistent deflection characteristics, enhancing the accuracy of cardiac activity maps.
Implementation Method 1
the depolarization and repolarization patterns of the heart are detectable as small changes in charge in skin cells that are measured using, for example, various cutaneous electrodes
Implementation Method 2
CDTPs can be determined using, for example, a dv/dt activation response
Implementation Method 3
a wavelet transform activation response
Data Source
AI summary
The present disclosure provides systems and methods for detecting cardiac activations of a patient. A system includes a data acquisition system (DAQ) communicatively coupled to an activation detection module. The DAQ detects an electrogram generated at an electrode disposed on or in the patient. The activation detection module is configured to receive the electrogram from the DAQ and compute an activation response. The activation detection module is further configured to determine a set of candidate detection time points (CDTPs) in the activation response. The activation detection module is configured to compute respective deflection characteristics for each CDTP. The activation detection module is configured to identify a group of final detection time points (FDTPs) among the set of CDTPs for a metric corresponding to the respective deflection characteristics. The group of FDTPs has similar deflection characteristics. The activation detection module is configured to compute metrics based on the group of FDTPs.