Physiological Electrogram Noise Model Derivation
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Solution Overview
Problem
Existing methods for analyzing physiological electrograms struggle to reliably distinguish between physiological signals and noise, particularly in cardiac arrhythmia analysis, due to interference from electrical noise, leading to challenges in detecting small individual potentials and setting appropriate amplitude thresholds.
Innovation Solution
A method that derives a noise model from portions of the electrogram where a physiological signal is absent, using cross-correlation with multiple templates to create a covariance matrix and eigenvectors, allowing for the identification of physiological signals by determining points that lie outside the noise model limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If amplitude threshold is set low to detect all peaks, then more physiological peaks are detected, but more noise peaks are also detected
Solution Approach 1:
The patent introduces a noise model as an intermediary between the raw electrogram signal and the detection threshold. This noise model, derived from segments without physiological signals, serves as a mediator that characterizes the noise distribution. By using this intermediary model, the system can set thresholds that account for noise characteristics without directly setting arbitrary amplitude values, thus reducing false detections while maintaining sensitivity to genuine physiological peaks.
Solution Approach 2:
The patent replaces the traditional mechanical approach of using fixed amplitude thresholds with a statistical model-based approach. Instead of relying on arbitrary amplitude cut-offs, the system uses probability density functions derived from noise segments to dynamically determine significant peaks. This substitution of the detection mechanism from fixed thresholds to statistical modeling allows for more reliable distinction between physiological signals and noise.
2Reliability
If amplitude threshold is set high to reduce noise detection, then fewer false peaks are detected, but physiological peaks with low amplitude are missed
Solution Approach 1:
The noise model acts as an intermediary that provides a probabilistic framework for threshold determination. Rather than using a fixed high threshold that may miss physiological peaks, the model calculates thresholds based on the actual noise distribution in the signal. This allows the system to adaptively set thresholds that are high enough to reject noise but low enough to capture genuine physiological signals, resolving the contradiction between reducing false detections and maintaining sensitivity.
Solution Approach 2:
The patent changes the parameter used for threshold determination from fixed amplitude values to statistical parameters (mean and standard deviation) derived from noise segments. By expressing thresholds in terms of statistical deviations from the noise mean, the system can dynamically adjust threshold levels based on the actual noise characteristics in each signal segment, thereby optimizing both false detection reduction and physiological peak detection across varying signal conditions.
3Measurement precision
If templates are correlated to reduce noise, then signal detection improves, but the correlator outputs become dependent and difficult to combine
Solution Approach 1:
The patent extracts and removes the noise component from the signal by deriving a noise model from segments without physiological signals. This extracted noise model is then used to characterize and reject noise in the full signal analysis. By taking out the noise characterization step separately, the system avoids the complexity of combining multiple correlated correlator outputs while still achieving effective noise reduction and improved signal detection accuracy.
Data Source
AI summary
Previous research has shown that the risk of sudden death due to cardiac arrhythmias can be predicted by observing the shape of recorded endocardial electrograms in response to pacing, and in particularly detecting certain small deflections in the recorded electrogram following early stimulation of the heart. A long standing problem has been the reliable detection of these small individual potentials because of the presence of noise in the recorded electrical signals created by other electrical equipment within a typical catheter laboratory. The solution described involves deriving a model of noise from a first portion of the electrogram in which a physiological signal is presumed to be absent, and transforming a second portion of the electrogram, presumed to contain a physiological signal, into the model of noise. The physiological signal can then be identified by identifying portions of signal within the second portion of the electrogram that do not conform to the model of noise.