Cardiac Dyssynchrony Analysis via ECG Derivative Time Differences
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Solution Overview
Problem
Current methods for visualizing and quantifying cardiac dyssynchrony are inadequate, particularly in selecting patients for cardiac resynchronization therapy (CRT), guiding pacemaker lead placement, and adjusting stimuli settings, with a significant portion of patients not responding to CRT.
Innovation Solution
A computer-implemented method that calculates derivative values of electrical signals from the skin to determine time differences between specific events, allowing for the quantification of cardiac dyssynchrony without transforming signals, enabling real-time analysis and efficient identification of dyssynchrony for improved CRT implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for evaluating cardiac electrical dyssynchrony are used (solving inverse problem of electrocardiography), then electrical activation times can be determined, but the process is complex and time-consuming
Solution Approach 1:
The patent extracts only the essential information needed for dyssynchrony assessment from the complex electrocardiographic signals. Instead of solving the full inverse problem to obtain complete activation time maps, the method extracts representative activation times from specific ECG leads that correlate with ventricular activation sequences, thereby simplifying the processing while maintaining diagnostic accuracy
Solution Approach 2:
The patent inverts the traditional approach by not trying to reconstruct the complete electrical activation pattern from surface ECG signals. Instead, it uses simplified time interval measurements from standard ECG leads and correlates these with dyssynchrony metrics, reversing the complex reconstruction process for a more efficient assessment
2Reliability
If cardiac resynchronization therapy is applied to all patients with dyssynchrony, then more patients may benefit, but 40-50% of patients are non-responders with little therapeutic effect
Solution Approach 1:
The patent implements a feedback mechanism where simplified dyssynchrony metrics derived from routine ECG signals are used to predict CRT response. By continuously monitoring these metrics and correlating them with therapeutic outcomes, the system refines patient selection criteria to identify responders more accurately, reducing the proportion of non-responders
Solution Approach 2:
The patent performs preliminary assessment of dyssynchrony characteristics using simple ECG time interval measurements before initiating CRT therapy. This preliminary action identifies patients most likely to respond to CRT, allowing for optimized patient selection and avoiding unnecessary treatments in non-responders
3Measurement precision
If complex signal transformation methods are used to analyze cardiac electrical activity, then detailed activation patterns can be obtained, but real-time analysis becomes difficult
Solution Approach 1:
The patent segments the complex electrocardiographic signal analysis into distinct, manageable time intervals corresponding to specific cardiac events (P wave, QRS complex, T wave). By analyzing dyssynchrony metrics within these segmented intervals using simple time interval measurements, the method achieves both precision and real-time capability without requiring complex full-signal transformations
Data Source
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AI summary
A method for analyzing a condition of a heart, comprises receiving a plurality of electrical signals, which are acquired by non-invasive measurement on the skin of a person or animal, each signal representing electrical activity in a respective region of the heart of the person or animal; calculating a derivative value of each signal at a plurality of time instances; selecting a plurality of the calculated derivative values of a first signal and determining a first point in time of a first event based on the selected derivative values; selecting a plurality of the calculated derivative values of a second signal and determining a second point in time of a second event, corresponding to the first event, based on the selected derivative values of the second signal, and calculating at least one measure based on a difference of the first point in time and the second point in time.