Convex Hull Analysis for Artifact Detection in Patient Monitoring
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Solution Overview
Problem
The rapid increase in data streams from healthcare technologies in intensive care units creates an information-overload challenge for healthcare staff, who face a shortage of resources, making it difficult to process and distinguish clinically-significant changes from insignificant changes or artifacts in patient states.
Innovation Solution
The development of intelligent modules capable of assimilating multiple data streams and using convex hull analysis to differentiate between clinically-significant changes and artifacts in patient monitoring, alerting operators to potential changes and detecting artifacts based on collective behavior of monitored signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple data streams from sensors and monitoring systems are collected to improve patient monitoring capability, then the comprehensiveness of patient state information is improved, but the information-overload challenge and processing difficulty increase
Solution Approach 1:
The patent combines multiple monitored signals (first monitored signal, second monitored signal, third monitored signal, fourth monitored signal) into pairs to create convex hulls. By merging signal pairs and analyzing their collective behavior through convex hull perturbations, the system processes multiple data streams more efficiently, reducing the information-overload challenge while maintaining comprehensive patient state monitoring.
2Measurement precision
If manual processing of monitoring data is performed to identify clinically-significant changes, then the accuracy of clinical assessment is maintained, but the workload and time consumption increase due to staff shortage
Solution Approach 1:
The system performs self-service by automatically analyzing monitoring signals and detecting clinically-significant changes without requiring manual processing. The intelligent module autonomously monitors convex hull perturbations across multiple signal pairs, distinguishes clinically-significant changes from artifacts, and generates alerts, thereby maintaining clinical assessment accuracy while significantly improving processing efficiency and reducing staff workload.
Solution Approach 2:
The patent replaces manual mechanical processing with an automated intelligent system. The system uses computational algorithms to calculate convex hulls, detect perturbations, and identify clinical changes, substituting human manual analysis with automated electronic processing. This maintains measurement precision while dramatically increasing productivity and reducing the burden on intensive care staff.
3Reliability
If artifact detection capability is enhanced to reduce false alerts, then the reliability of monitoring system is improved, but the complexity of signal analysis increases
Solution Approach 1:
The patent segments the analysis by dividing monitored signals into multiple pairs (first and second monitored signal pairs, third and fourth monitored signal pairs) and calculating separate convex hulls for each pair. By segmenting the analysis into independent convex hull evaluations, the system enhances artifact detection capability - if only one convex hull is perturbed, it indicates an artifact. This segmentation approach improves alert accuracy while managing analysis complexity through modular, systematic evaluation of signal pairs.
Data Source
AI summary
A method for monitoring a patient (110) includes determining (114) convex hulls for pairs of monitored signals from the patient, and determining whether a perturbation has occurred (115, 116) in one or more of the convex hulls. This exemplary embodiment (110) can also include alerting an operator that a clinically significant change may have occurred (117) in the patient if each of the convex hulls has been perturbed. If only a subset of the convex hulls is perturbed, an artifact has probably occurred (118).