Cardiac Signal Analysis via Hilbert Transform State Space Trajectory
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Solution Overview
Problem
Traditional automated ECG signal analysis tools rely on correlation-based template matching and empirical decision rules, which are not sufficient for accurate identification of cardiac signal components and detection of physiological conditions like ventricular fibrillation, especially in varying ECG morphologies and low amplitude signals.
Innovation Solution
A machine-implemented method using a Hilbert transform to generate a partial state space trajectory, separating phase components, and combining amplitude and phase properties to identify physiological information, including locating P-wave, QRS complex, and T-wave components, and detecting ventricular fibrillation by monitoring periodicity in the momentum of the trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If correlation-based template matching and empirical decision rules are used for ECG analysis, then the method is simple to implement, but the accuracy in identifying cardiac signal components and detecting physiological conditions deteriorates
Solution Approach 1:
The patent transforms the one-dimensional ECG signal into a two-dimensional state space trajectory by applying the Hilbert transform. This dimensional transformation enables the system to capture both amplitude and phase information simultaneously, improving the accuracy of cardiac signal component identification without significantly increasing implementation complexity
Solution Approach 2:
The Hilbert transform serves as an intermediary that converts the raw ECG signal into a state space representation with additional phase information. This intermediary transformation enables more accurate detection of cardiac components by providing extra dimensional data that enhances the measurement precision while maintaining algorithm simplicity
2Use of energy by moving object
If traditional ECG analysis methods are used, then the computational resources required are minimal, but the reliability in detecting physiological conditions like ventricular fibrillation deteriorates
Solution Approach 1:
The patent changes the parameter space by introducing phase information through the Hilbert transform. This parameter transformation enables more reliable detection of physiological conditions such as ventricular fibrillation by providing additional diagnostic dimensions, while the computational overhead remains manageable for real-time applications
3Ease of operation
If amplitude-based detection is used for identifying cardiac components, then the method is straightforward, but the performance in low amplitude signals deteriorates
Solution Approach 1:
By transforming the signal into state space and extracting phase information, the patent creates a new detection dimension that is independent of signal amplitude. This allows the system to identify cardiac components reliably even when amplitude is low, as the phase characteristics remain detectable regardless of signal strength
Solution Approach 2:
The patent combines amplitude and phase information into a composite state space representation. This composite approach allows the system to leverage phase information for detection in low amplitude conditions, effectively compensating for the limitations of amplitude-based methods alone
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables improved accuracy in identifying cardiac signal components and detecting ventricular fibrillation, even in low amplitude signals, with increased robustness and precision in determining time intervals and physiological conditions, suitable for real-time systems with limited computational resources.
Implementation Method 1
applying a Hilbert (H) transform to the time series x(t) to obtain H(x(t)), wherein x(t) and H(x(t)) together forming a partial state space trajectory
Data Source
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AI summary
Systems and techniques relating to locating cardiac wave forms in a cardiac signal, and to detecting a physiological condition, such as ventricular fibrillation. In general, in one aspect, a machine-implemented method includes obtaining a sensed cardiac signal of an organism, the sensed cardiac signal comprising a time series x(t); applying a Hilbert (H) transform to the time series x(t) to obtain H(x(t)), wherein x(t) and H(x(t)) together forming a partial state space trajectory; determining a speed of trajectory, for the sensed cardiac signal, from the partial state space trajectory; and identifying physiological information concerning the organism based on a combination of first and second signal elements, the first signal element including a phase property or an amplitude property of the speed of trajectory, and the second signal element including an amplitude property of the partial state space trajectory.