Cellular Electrical Potential Estimation via Extended Kalman Filter
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Solution Overview
Problem
Current methods for modeling electrocardiographic (ECG) data struggle to accurately account for dynamic cardiac electrical phenomena and anatomical structures, leading to reduced capability in identifying heart abnormalities due to sensitivity to measurement noise and model errors, and fail to account for periodic behavior within the heart beat cycle.
Innovation Solution
The use of an extended Kalman filter to dynamically adjust model parameters of an electrical source model, combining anatomical data with ECG or body-surface-potential signals to estimate myocardial cell electrical potentials, allowing for precise mapping and visualization of heart activity, thereby addressing the inverse problem of solving for cardiac electrophysiological activity from surface measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ECG modeling methods are used, then the system is simple and easy to operate, but the measurement precision and reliability are reduced due to sensitivity to measurement noise and model errors
Solution Approach 1:
The patent applies dynamics by using an extended Kalman filter to dynamically adjust model parameters in real-time based on measured ECG signals. The state estimator continuously updates the electrical source model parameters (such as dipole moment, orientation, and position) to adapt to changing cardiac electrical activity, thereby improving measurement precision while maintaining model accuracy through dynamic adaptation rather than static assumptions
Solution Approach 2:
The patent implements feedback by using measured ECG signals to continuously adjust and refine the electrical source model parameters through the extended Kalman filter. The feedback loop compares measured signals with modeled signals and uses the difference to update model parameters, reducing sensitivity to measurement noise and model errors while improving the accuracy of cardiac electrical activity identification
2Reliability
If a detailed electrical source model is used to improve measurement precision, then the reliability of heart abnormality identification improves, but the device complexity increases
Solution Approach 1:
The patent applies parameter changes by using the extended Kalman filter to dynamically adjust a limited set of key electrical source model parameters (dipole moment, orientation, position) rather than requiring a detailed complex model. This approach improves reliability by continuously optimizing the parameters based on measured ECG signals while avoiding the complexity of modeling every detail of cardiac electrophysiology
Solution Approach 2:
The patent segments the complex problem of cardiac electrical activity modeling into key independent parameters (dipole moment, orientation, position) that can be adjusted separately through the extended Kalman filter. This segmentation allows the system to achieve high reliability in identifying heart abnormalities by focusing on the most critical parameters rather than attempting to model all aspects of cardiac electrical activity
3Productivity
If static ECG modeling is used, then the system is computationally efficient, but it fails to account for periodic behavior within the heart beat cycle
Solution Approach 1:
The patent applies dynamics by using the extended Kalman filter to dynamically adjust model parameters at each time step of the ECG signal, capturing the periodic behavior within the heart beat cycle. The dynamic state estimator continuously updates parameters based on the temporal structure of the ECG signal, thereby preserving information about periodic cardiac activity while maintaining computational efficiency through efficient filtering algorithms
Data Source
AI summary
A method is provided for determining cellular electrical potentials using a state estimator. The state estimator is generated using at least an electrical source model and an electrical conduction model. One or more parameters or states of the state estimator are adjusted based on a measured electrocardiographic and/or a measured body-surface-potential signal. The electrical potential of one or more cells is determined based on the one or more adjusted parameters or states. In one aspect of the present technique, one or more representations of an organ comprising the one or more cells is generated such that the electrical potential or its deriving characteristic of the one or more cells is visually indicated.