Cardiac Signal Processing for Activation Time Mapping
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Solution Overview
Problem
Current medical devices for mapping cardiac electrical activity require extensive user input for processing large numbers of electrogram signals, leading to lengthy procedure times and potential inaccuracies due to the need to analyze signals from multiple electrodes, which can result in ineffective or harmful ablation therapy.
Innovation Solution
A method and system that process cardiac electrical signals using techniques such as wavelet transforms, rectification, low-pass filtering, statistical analysis, and clustering to reduce the number of signals requiring user input, identifying a characteristic signal and determining activation times for both known and unknown data points, thereby generating an activation time map with reduced user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive user input is required for processing electrogram signals from multiple electrodes, then measurement precision can be improved, but procedure time increases and productivity decreases
Solution Approach 1:
The system automatically processes electrogram signals by separating them into first and second groups without requiring extensive user input. The automated signal processing includes wavelet transforms, rectification, low-pass filtering, and clustering algorithms that independently analyze and process signals from multiple electrodes, reducing the need for manual intervention while maintaining processing accuracy
Solution Approach 2:
The system performs preliminary signal processing actions by automatically separating electrogram signals into distinct groups before final analysis. The automated clustering and classification processes prepare the data in advance, identifying characteristic signals and determining activation times without requiring manual step-by-step analysis for each signal
2Reliability
If manual analysis of signals from multiple electrodes is performed, then reliability can be improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system autonomously performs signal analysis and classification by automatically separating electrogram signals into first and second groups. The automated algorithms including wavelet transforms, rectification, low-pass filtering, and clustering processes eliminate the need for manual electrode signal analysis while maintaining reliable ablation therapy guidance through automated activation time determination
Solution Approach 2:
The patent replaces manual mechanical analysis of electrical signals with automated computational algorithms. The system uses digital signal processing techniques including wavelet transforms, statistical analysis, and clustering algorithms to automatically identify characteristic signals and determine activation times, substituting human operator judgment with automated computational processes
Data Source
AI summary
Medical devices and methods for making and using medical devices are disclosed. A method of mapping electrical activity of a heart may comprise sensing a plurality of signals with a plurality of electrodes positioned within the heart. The method may further comprise separating the plurality of signals into a first group of signals and a second group of signals, and generating a data set that includes at least one known data point and one or more unknown data points. In some examples, the at least one known data point is generated based on the first group of signals.