Cardiac Capture Classification Using Frequency-Filtered Pacing Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cardiac stimulation devices struggle to identify and adjust to the minimum power and rate necessary for inducing His bundle capture, and classify responses to pacing impulses effectively, particularly in cases of conduction disorders and dilated cardiomyopathy.
Innovation Solution
A cardiac stimulation system with a stimulating electrode and sensing electrodes that applies pacing impulses, measures responses, and analyzes frequency characteristics to determine capture type, using filters to modify frequencies indicative of selective or myocardium-only capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of capture responses is used, then clinical expertise is required for accurate classification, but the process is time-consuming and operator-dependent
Solution Approach 1:
The system automatically classifies capture responses using embedded algorithms that analyze electrogram signals without requiring external clinical intervention. The device self-determines capture type (His bundle vs. myocardium) by processing signal characteristics such as amplitude, morphology, and timing, thereby eliminating operator dependency and reducing classification time while maintaining accuracy
Solution Approach 2:
The patent replaces manual visual inspection and expert judgment with automated electronic signal processing. Algorithms analyze electrogram waveforms, calculate derived parameters, and classify capture types through computational methods, substituting the mechanical process of manual review with electronic automation that operates rapidly and consistently
2Reliability
If high pacing power is used to ensure His bundle capture, then capture reliability is improved, but energy consumption increases and myocardial damage risk increases
Solution Approach 1:
The system continuously monitors electrogram signals during pacing and uses automated classification to provide feedback on capture status. Based on this feedback, the device dynamically adjusts pacing parameters including output power and pulse width, delivering only the minimum necessary energy to achieve and maintain reliable His bundle capture, thereby optimizing the balance between capture reliability and energy consumption
Solution Approach 2:
The patent implements dynamic adjustment of pacing parameters based on real-time signal analysis. The system adapts output power, pulse duration, and rate according to detected capture characteristics, transitioning between different pacing configurations to maintain reliable capture while minimizing energy use and avoiding excessive power delivery that could cause myocardial damage
3Measurement precision
If frequency analysis is used to classify capture types, then classification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the electrogram signal into distinct components (His bundle potential, ventricular activation, atrial activity) and analyzes specific temporal and frequency characteristics of each segment. By dividing the complex signal into manageable portions with distinct analytical approaches, the system achieves high classification accuracy while keeping individual processing tasks computationally tractable
Solution Approach 2:
The system transforms the time-domain electrogram signal into the frequency domain using spectral analysis, adding a frequency dimension to the analysis. This dimensional transformation reveals characteristic frequency patterns that differentiate His bundle capture from myocardium capture, enhancing classification accuracy while using standard signal processing techniques that balance computational requirements
4Productivity
If automated capture classification is implemented, then classification speed is improved, but algorithm complexity increases
Solution Approach 1:
The system performs preliminary signal preprocessing including filtering, baseline removal, and feature extraction before classification. By preparing the signal in advance and pre-defining classification thresholds and criteria, the automated algorithm operates efficiently at high speed without requiring complex real-time decision-making, thus achieving fast classification with manageable algorithmic complexity
Data Source
AI summary
Systems and methods for pacing and classifying response to pacing impulses include applying, using a pulse generator, an impulse through a stimulating electrode to induce a response from a patient heart. A response to the impulse is measured using at least one sensing electrode and characteristics of the response are analyzed to determine whether capture has occurred and, if so, what type of capture has occurred. To facilitate analysis, the response measured from the patient heart may also be filtered to pass or stop frequencies indicative of certain capture types.