Atrial Fibrillation Classification via Dominant Frequency Window
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Solution Overview
Problem
Current methods for classifying atrial fibrillation using ECG data are not robust, as they rely on finding a single dominant frequency, which inadequately describes the complex and chaotic nature of atrial fibrillation, leading to suboptimal treatment strategies and outcome predictions.
Innovation Solution
The method involves decomposing ECG data into frequency and amplitude dimensions to identify a dominant window containing a predefined percentage of signal power, rather than focusing on a single frequency, allowing for a more robust classification of atrial fibrillation complexity and severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single dominant frequency is identified from the ECG spectrum, then the classification method is simple to implement, but the classification accuracy and robustness deteriorate due to the chaotic nature of atrial fibrillation
Solution Approach 1:
The patent segments the frequency spectrum into multiple frequency bands instead of identifying a single dominant frequency. Each frequency band is analyzed separately to determine its contribution to the atrial fibrillation signal, allowing for a more comprehensive and accurate classification that captures the chaotic multi-frequency nature of AF.
Solution Approach 2:
The patent transitions from a one-dimensional approach (single frequency) to a two-dimensional approach by introducing both frequency and amplitude dimensions. The amplitude criterion based on sum of amplitudes adds a new dimension for evaluating frequency components, enabling more robust classification that accounts for the complex spectral distribution of atrial fibrillation.
2Device complexity
If the classification focuses on a single peak frequency, then the computational complexity is low, but the reliability of classification deteriorates due to random and temporary peaks in less relevant frequencies
Solution Approach 1:
The patent merges multiple frequency components that meet the amplitude criterion into a unified classification result. Instead of relying on a single peak frequency that may be spurious, the method combines evidence from multiple frequency bands, each contributing to the overall classification decision, thereby improving reliability through aggregation of multiple indicators.
Solution Approach 2:
The patent implements a feedback mechanism where the amplitude criterion is evaluated iteratively across different frequency bands. The classification process continuously refines its assessment by comparing the sum of amplitudes against the criterion, allowing the system to adjust and confirm classifications based on cumulative evidence from multiple frequency components rather than a single measurement.
3Reliability
If a dominant window containing multiple frequencies is used instead of a single frequency, then the classification robustness improves, but the complexity of the analysis method increases
Solution Approach 1:
The patent changes the evaluation parameter from single-frequency amplitude to sum of amplitudes across multiple frequencies within a dominant window. This parameter transformation allows the method to capture the distributed energy characteristic of atrial fibrillation while maintaining a clear decision criterion. The amplitude criterion based on summed amplitudes provides a straightforward metric that improves robustness without requiring excessively complex analysis.
Data Source
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AI summary
The invention relates to a method for classifying atrial fibrillation by analysing ECG data (1) via a control system (2), wherein in a decomposition step the control system (2) decomposes the ECG data (1) by a frequency decomposition thereby generating frequency data (7), in particular a frequency spectrum, comprising a frequency dimension (f) and an amplitude dimension (a). It is proposed that in an analysis step the control system (2) identifies in the frequency data (7) a dominant window (8) having a width (9) in the frequency dimension (f) and meeting an amplitude criterion, that the amplitude criterion is based on a sum of amplitudes inside the dominant window (8), that the control system (2) identifies the dominant window (8) by searching on at least a section, in particular a pre-defined section, of the frequency dimension (f) for a window fulfilling the amplitude criterion and that based on the dominant window (8) the control system (2) determines the classification of atrial fibrillation in the ECG data (1).