Uroflowmetry Signal Morphology for Artifact Detection and Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Uroflowmetry data is often contaminated by noise and artifacts from external events, which can lead to inaccurate interpretations and hinder accurate diagnosis of urinary tract health issues.
Innovation Solution
A noise artifact detection method is employed to identify and remove artifacts from uroflowmetry data by analyzing the morphology of waveforms, using bandpass filtering and algorithms to detect and characterize positive and negative form artifacts, and differentiate them from physiological events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If uroflowmetry data is collected from external events, then the quantity of data is increased, but noise and artifacts contaminate the data reducing measurement precision
Solution Approach 1:
The patent segments the continuous uroflowmetry signal into discrete morphological components (positive form artifacts, negative form artifacts, and physiological events). By dividing the signal analysis into distinct morphological categories, the system can identify and separate artifacts from valid physiological data, thereby maintaining measurement precision while utilizing comprehensive data collection.
Solution Approach 2:
The patent introduces morphological analysis algorithms as an intermediary between raw data collection and final interpretation. This intermediary processing layer characterizes signal patterns to distinguish artifacts from physiological events, acting as a mediator that filters contaminated data while preserving valid measurements, thus resolving the contradiction between data quantity and accuracy.
2Measurement precision
If artifact detection algorithms are applied to uroflowmetry data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically characterizing and identifying artifacts through morphological analysis of the signal itself. The algorithms examine the inherent properties of the uroflowmetry data (waveform shapes, durations, amplitudes) to distinguish artifacts from physiological events without requiring external intervention or complex additional hardware, thereby improving precision while limiting complexity growth.
Solution Approach 2:
The patent applies parameter changes by analyzing multiple signal characteristics (morphology, duration, amplitude, waveform shape) rather than relying on a single parameter. This multi-parameter approach enables accurate artifact detection through software analysis of existing signal properties, avoiding the need for additional complex hardware sensors while achieving high measurement precision.
3Loss of information
If bandpass filtering and morphological analysis are used, then noise artifacts are removed improving data purity, but processing time increases
Solution Approach 1:
The patent applies preliminary action by performing bandpass filtering and morphological characterization on the uroflowmetry signal as it is being collected, rather than processing the entire dataset after acquisition. This real-time or near-real-time processing approach removes artifacts during data collection, maintaining data purity while minimizing additional processing time through efficient algorithmic implementation.
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
Aspects of the disclosure are directed toward a method for providing uroflowmeter data. The method comprises receiving volume sample data representative of volume sample data from a uroflowmeter device, calculating the slope of the volume sample data, and performing additional actions if the calculated slope reaches a trigger threshold. If the calculated slope reaches a trigger threshold, the method may further determine if an artifact is present in the volume sample data. This may include comparing the morphology of the potential artifact to morphologies of known artifacts and comparing the value of the volume sample data before and after the potential artifact. If an artifact is determined to be present in the volume sample data and the volume sample data before the potential artifact is less than or equal to the volume sample data after the potential artifact, remove the portion of the volume sample data which represents the artifact.