Myogram Determination from ECG Signal Using Covariance Matrix
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Solution Overview
Problem
Existing systems for measuring respiratory activity from ECG signals struggle to isolate muscle activity information from individual electrodes, leading to incomplete data due to combined myographic power signals from multiple electrodes, which can mask specific respiratory or muscle activity types.
Innovation Solution
A method and system that filter ECG sample signals to remove cardiac components and generate combined myographic power signals, then apply these signals within a covariance matrix to calculate individual myographic power signals for each electrode, allowing for the isolation of respiratory and muscle activity data from individual electrodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If combined myographic power signals from multiple electrodes are used, then the quantity of respiratory activity data is improved, but the measurement precision of individual electrode muscle activity deteriorates
Solution Approach 1:
The patent applies segmentation by decomposing the combined myographic power signals from multiple electrodes into individual electrode-specific myographic power signals through matrix factorization techniques. This allows the system to maintain the comprehensive respiratory activity data from all electrodes while simultaneously extracting precise individual electrode contributions, thereby resolving the contradiction between data quantity and measurement precision.
2Reliability
If ECG signals are filtered to remove cardiac components, then the purity of myographic signal is improved, but the loss of information in the signal processing deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting filtering parameters and using adaptive signal processing techniques that modify the filtering approach based on the specific characteristics of the ECG and myographic signals. This allows optimal separation of cardiac and myographic components while preserving relevant physiological information, resolving the contradiction between signal purity and information retention.
Data Source
AI summary
Systems and methods for measuring signals representative of muscle activity are provided. One method includes detecting an ECG signal through a plurality of electrodes. The ECG signal includes a plurality of ECG sample signals, and each ECG sample signal is a bipolar signal associated with two of the plurality of electrodes and includes a cardiac signal component and a myographic signal component. The method further includes filtering each of the ECG sample signals to remove at least a portion of the cardiac signal component and generate a combined myographic power signal for the two of the plurality of electrodes with which the ECG sample signal is associated. Each combined myographic power signal represents a myographic potential between the two electrodes. The method further includes calculating individual myographic power signals for each of the plurality of electrodes by applying the combined myographic power signals within a covariance matrix.