Ventricular Fibrillation Detection via Hilbert Transform
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Solution Overview
Problem
Existing cardiac monitoring systems face challenges in distinguishing between ventricular fibrillation events and noise in physiological signals, as these events can appear similar and are difficult to differentiate.
Innovation Solution
The method involves obtaining an ECG signal, applying a transform such as the Hilbert transformation to create an analytical pair, determining the speed-amplitude, and identifying the onset and offset of ventricular fibrillation events based on cost function values and threshold crossings within specific parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ECG monitoring methods are used to detect ventricular fibrillation, then the detection process is simple, but the ability to distinguish VF events from noise is poor
Solution Approach 1:
The detection method segments the ECG signal analysis into multiple independent components: HF content calculation, LF content calculation, VF index computation, and threshold comparison. Each component can be processed separately and combined to form the final detection result, improving accuracy while maintaining manageable complexity
Solution Approach 2:
The patent introduces a new dimension of analysis by calculating the difference between high-frequency and low-frequency energy content (VF index = HF - LF). This transforms the traditional single-dimension amplitude analysis into a multi-dimensional spectral analysis, enabling better differentiation of VF events from noise based on their distinct frequency characteristics
2Reliability
If multiple parameters and thresholds are used to identify VF events, then the distinction between VF and noise improves, but the computational complexity increases
Solution Approach 1:
The system performs preliminary calculations of HF content and LF content over defined time windows before computing the final VF index. These pre-computed values are stored and then compared against predetermined thresholds, allowing the detection algorithm to make reliable decisions without performing complex real-time computations at the moment of detection
Solution Approach 2:
The patent employs multiple parameters (HF content, LF content, VF index) with different predetermined thresholds to characterize VF events. By changing and comparing multiple parameters simultaneously, the system achieves high reliability in distinguishing VF from noise, with each parameter serving as an independent verification criterion
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 effectively identifies ventricular fibrillation events while distinguishing them from noise, enhancing the accuracy of cardiac signal analysis and aiding in timely medical intervention.
Implementation Method 1
applying a transform to the ECG signal to obtain an analytical pair, the analytical pair including the ECG signal and the transformed ECG signal
Data Source
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AI summary
Systems and techniques are disclosed for determining the onset and offset of a ventricular fibrillation event and for distinguishing ventricular fibrillation from noise. An electrocardiogram (ECG) signal is obtained (122, 136); a Hilbert transform is applied to the ECG signal to obtain an analytical pair, the analytical pair including the ECG signal and the transformed ECG signal; a speed-amplitude is determined from the analytical pair; and an onset (131) or offset (145) of a ventricular fibrillation event is identified based at least one of a value of a cost function of the speed-amplitude over a window and a quantity of occurrences the speed-amplitude crosses a threshold over the window.