Waveform Fiducial Localization with Sparse Annotation Heatmaps
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Solution Overview
Problem
Current waveform analysis processes require full annotation of each fiducial before transforming a waveform into a heatmap, which is time-consuming and difficult.
Innovation Solution
A method that allows for the transformation of waveforms into heatmaps using less than full annotation, specifically requiring only one or two beats per waveform to be annotated, utilizing a decaying function and sparse annotations to construct heatmaps, and employing machine learning models to generalize to unannotated portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full annotation of each fiducial is performed before waveform transformation, then heatmap accuracy is improved, but annotation time and complexity increase significantly
Solution Approach 1:
The patent applies partial action by requiring only one or two beats per waveform to be annotated instead of full annotation. This sparse annotation approach maintains sufficient heatmap accuracy while dramatically reducing the time and effort required for annotation, directly resolving the contradiction between accuracy and time consumption
2Manufacturing precision
If full annotation of each fiducial is performed, then heatmap quality is improved, but the difficulty and complexity of the process increase
Solution Approach 1:
By requiring only partial annotation (one or two beats) rather than complete annotation of all fiducials, the patent reduces annotation complexity while maintaining sufficient heatmap quality through the use of decaying functions and machine learning generalization
3Productivity
If sparse annotation is used, then annotation time is reduced, but the ability to generalize to unannotated portions becomes more challenging
Solution Approach 1:
The patent introduces decaying functions as an intermediary mechanism that bridges sparse annotations and unannotated portions. The decaying function propagates information from annotated beats to surrounding unannotated regions, enabling generalization while maintaining high transformation throughput
Solution Approach 2:
The patent employs machine learning models that learn optimal parameters for heatmap generation from sparsely annotated data. By adjusting model parameters during training on partially annotated waveforms, the system achieves reliable generalization to unannotated portions while maintaining high productivity
Data Source
AI summary
A system and method are described. An illustrative method includes receiving a digital dataset representing waveform data comprising the waveform, windowing the digital dataset such that no more than two periods of the waveform exist within a windowed version of the digital dataset, providing the windowed version of the dataset to a trained machine learning model, receiving an output signal from the trained machine learning model, where the output signal is generated by the machine learning model in response to the trained machine learning model processing the windowed version of the dataset, generating the heatmap with the output signal received from the trained machine learning model, and causing a device to take action based on the heatmap.