Ultrasound Data Processor for Artifact-Resistant Hemodynamic Monitoring
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Solution Overview
Problem
Ultrasound data used for hemodynamic measurements is prone to artifacts from external sources like electro-knives in perioperative environments, which can lead to inaccurate blood flow parameter calculations, making it difficult to distinguish between clinically relevant and non-relevant variations.
Innovation Solution
An ultrasound data processor that applies Fourier spectral analysis and wavelet decomposition to identify periodic patterns in the data, feeding the results to a classifier to determine the reliability of the measurements, enabling differentiation between clinically relevant and artifact-induced variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ultrasound data is used for continuous hemodynamic monitoring, then real-time patient monitoring capability is improved, but reliability of measurements deteriorates due to artifacts from external sources like electro-knives
Solution Approach 1:
The system performs preliminary analysis of ultrasound data using spectral and wavelet decomposition techniques before generating hemodynamic measurements. This preliminary action identifies artifacts and their characteristics in advance, allowing the system to distinguish between clinically relevant variations and artifact-induced distortions before final measurements are derived.
Solution Approach 2:
The patent introduces an intermediary analysis layer between raw ultrasound data and final hemodynamic measurements. This intermediary layer uses spectral and wavelet decomposition to create processed features that mediate between the raw signal and clinical parameters, enabling artifact detection and measurement generation to coexist without interference.
2Reliability
If spectral and wavelet analysis procedures are applied to ultrasound data, then artifact detection capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex analysis process into distinct modular components: spectral decomposition module, wavelet decomposition module, and artifact detection module. Each module performs a specific function and can be independently optimized or implemented, reducing overall system complexity while maintaining comprehensive artifact detection capability.
Solution Approach 2:
The system transforms one-dimensional time-domain ultrasound signals into multi-dimensional frequency and time-frequency representations through spectral and wavelet analysis. This dimensional transformation enables artifact detection in additional dimensions (frequency spectrum, wavelet coefficients) without requiring proportionally increased computational complexity in the time domain.
3Measurement precision
If multiple analysis procedures are applied to distinguish artifact types, then measurement accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary spectral and wavelet decomposition to extract characteristic features of artifacts before conducting detailed analysis. This preliminary action identifies and classifies artifact types early in the processing pipeline, allowing subsequent analysis to focus computational resources only on regions requiring detailed examination, thereby reducing overall processing time while maintaining high measurement accuracy.
Data Source
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AI summary
An ultrasound data processor for assessing reliability of ultrasound data for determining blood flow values for a patient. The processor is adapted to apply at least a spectral analysis procedure and a wavelet decomposition procedure to input ultrasound data (or data or signals derived therefrom), and to feed the output(s) of said procedures to a classifier algorithm configured to generate a reliability indicator based on these input information. Both spectral analysis and wavelet decomposition are signal analysis techniques which assess periodic properties of data and signals, this being particularly suited to detecting background noise artefacts (such as those generated by electrical knives used in surgery) which do not change the morphology or shape of the signals but do impose a background distortion.