Multi-channel cardiac measurement with adaptive channel replacement
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Solution Overview
Problem
Current multi-channel cardiac electrogram (MCCE) processing algorithms provide inaccurate measurements, leading to misleading activation maps and prolonged cardiac procedures, as they are simplistic and prone to noise and variability in cardiac signals.
Innovation Solution
An automatic method for accurately measuring cardiac parameters like cycle length and local activation time, which involves selecting reference and mapping channels, monitoring signal quality, and replacing sub-standard channels to ensure reliable data, using techniques such as velocity-dependent signal generation and autocorrelation to maintain timing information accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplistic MCCE processing algorithms are used, then device complexity is reduced, but measurement precision deteriorates leading to inaccurate cardiac parameter measurements
Solution Approach 1:
The algorithm segments the MCCE signal processing into multiple distinct stages: quality assessment of individual channels, selective channel replacement, velocity-dependent signal generation, and autocorrelation-based parameter extraction. This segmentation allows each stage to be optimized independently, improving measurement precision without requiring the entire system to be overly complex.
Solution Approach 2:
The algorithm performs preliminary quality assessment and channel selection before the main parameter extraction process. By pre-identifying and replacing substandard channels, and pre-generating velocity-dependent signals, the main processing stage can focus on accurate measurement rather than signal validation, improving overall precision.
2Reliability
If multiple channels are monitored and replaced when quality degrades, then reliability improves, but device complexity increases
Solution Approach 1:
The system performs self-diagnosis and self-correction by automatically assessing the quality of each MCCE channel and replacing degraded channels with alternative channels from the multi-channel array. This self-service mechanism improves reliability without requiring external intervention or overly complex manual monitoring systems.
Solution Approach 2:
The algorithm dynamically changes the operational parameters by switching between different channels based on their quality metrics. When a channel's signal quality deteriorates below a threshold, the system changes to a different channel's data, maintaining reliable measurements without requiring complex hardware modifications.
3Measurement precision
If velocity-dependent signal generation and autocorrelation are used, then measurement precision improves, but use of energy increases
Solution Approach 1:
The algorithm applies velocity-dependent signal generation and autocorrelation selectively to the essential timing-critical portions of the signal rather than processing the entire signal spectrum with equal intensity. This partial action approach maintains high timing precision while reducing the total computational energy required compared to exhaustive signal processing methods.
4Productivity
If rapid parameter measurement is implemented, then productivity increases, but measurement precision may deteriorate
Solution Approach 1:
By pre-assessing channel quality and pre-generating velocity-dependent signals before the main parameter extraction, the system eliminates time-consuming validation steps during the critical measurement phase. This preliminary preparation enables rapid parameter extraction without sacrificing precision, as the data quality is already guaranteed by the pre-screening process.
Solution Approach 2:
The algorithm replaces traditional mechanical or manual signal analysis methods with automated digital signal processing techniques. The use of automated autocorrelation and velocity-dependent signal generation allows rapid computation of cardiac parameters with high precision, eliminating the time-consuming manual analysis that would compromise productivity.
Data Source
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AI summary
An automatic method of determining local activation time (LAT) in multi-channel cardiac electrogram signals including a plurality of cardiac channels, the method comprising: (a) storing the cardiac channel signals; (b) selecting a mapping channel, a ventricular channel, and a reference channel from among the plurality of cardiac channels; (c) using the selected channels to compute first LAT values at a plurality of mapping-channel locations; (d) monitoring the quality of at least one selected channel; (e) if the quality of a monitored cardiac channel falls below a standard, replacing the sub-standard channel with another channel of the plurality of channels having an above-standard quality; and (f) computing second LAT values based on the replacement cardiac channel.