3D Physiological Signal Visualization for Abnormality Detection
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Solution Overview
Problem
Clinical diagnostic and monitoring applications face challenges in timely and accurate identification of abnormalities in repetitive physiological signals, such as ECG waveforms, due to the complexity and volume of data that require immediate differentiation between normal and abnormal portions.
Innovation Solution
A system and method that utilize three-dimensional visual outputs to automatically trace abnormalities in physiological signals by receiving and processing data from sensors, determining signal intervals, segmenting the signals, identifying potential abnormalities, and displaying them graphically distinguished from normal segments over time, allowing for immediate visualization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional two-dimensional display methods are used to show physiological signals, then the display is simple and easy to implement, but the ability to immediately differentiate normal and abnormal portions is insufficient
Solution Approach 1:
The patent transitions from traditional two-dimensional signal display to a three-dimensional visual output where the vertical dimension represents time, the horizontal axis represents signal amplitude, and the depth dimension represents signal intensity or abnormality severity. This dimensional addition enables immediate visual differentiation of abnormal portions while maintaining computational feasibility through standardized 3D rendering techniques.
Solution Approach 2:
The patent employs color coding to graphically distinguish abnormal signal portions from normal segments. Different colors or intensity levels are assigned to represent various abnormality types (e.g., ST elevation, depression, arrhythmia), enabling rapid visual identification and classification of cardiac anomalies without requiring complex additional hardware.
2Measurement precision
If detailed analysis of all physiological signal data is performed, then diagnostic accuracy is improved, but the time required for analysis increases
Solution Approach 1:
The patent extracts and isolates abnormal portions of physiological signals from the complete dataset, separating them from normal segments. This extraction approach allows clinicians to focus analysis on only the relevant abnormal portions rather than reviewing entire lengthy recordings, thereby maintaining high diagnostic accuracy while significantly reducing analysis time.
Solution Approach 2:
The system performs preliminary automated analysis and identification of abnormal signal portions before clinical review. By pre-processing the data to flag and visually highlight potential abnormalities, the system prepares the information in advance, enabling clinicians to immediately focus on confirmed abnormal segments without performing initial screening, thus reducing overall analysis time while preserving diagnostic accuracy.
3Measurement precision
If complex signal processing algorithms are used to identify abnormalities, then detection precision is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent segments the physiological signal into discrete portions based on detected abnormalities, processing and displaying each segment independently. This segmentation approach allows the use of specialized, optimized algorithms for specific abnormality types rather than requiring a single complex universal processor, thereby improving detection precision for each segment while reducing overall computational complexity through modular processing.
Data Source
AI summary
The present disclosure describes various systems and methods of modifying a display to automatically visually trace an abnormality associated with a physiological signal received from a patient (i.e., subject). In particular aspects, the systems and methods described herein utilize three-dimensional display outputs of physiological signals that allow for the immediate differentiation between normal portions of the physiological signal and abnormal portions of the physiological signal.