Classifying of a position of a catheter in relation to a diaphragm
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Solution Overview
Problem
Existing methods for accurately positioning nasogastric catheters, particularly in sedated patients, are hindered by the dominance of electrocardiographic signals over electromyographic signals, leading to difficulty in detecting correct placement and reliance on clinician attention, which can result in complications such as aspiration or perforation.
Innovation Solution
A machine learning algorithm trained using bioelectrical signals from a catheter, divided into subsets based on electrode position relative to the diaphragm, to classify correct and incorrect positions, with techniques to balance training data and reduce electromyographic signal reliance, allowing continuous and automatic monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electromyographic signals are used to detect catheter position, then positioning accuracy is improved, but signal detection becomes difficult when patients are sedated and not breathing spontaneously
Solution Approach 1:
The patent uses electrocardiographic signals as an intermediary to indirectly indicate catheter position relative to the diaphragm. Instead of directly measuring electromyographic signals from the diaphragm (which may be absent in sedated patients), the system detects ECG signals that are modulated by the mechanical movement of the diaphragm during breathing, providing a reliable proxy measurement.
Solution Approach 2:
The patent replaces the mechanical/biological measurement system (direct electromyographic signal detection from diaphragm muscle activity) with an electrical signal-based system (electrocardiographic signal analysis). By analyzing changes in ECG signal morphology and amplitude caused by diaphragmatic movement, the system achieves positioning without relying on direct muscle electrical activity.
2Use of energy by moving object
If electrocardiographic signals are monitored to assess catheter position, then signal strength is improved, but positioning precision deteriorates due to signal dominance masking electromyographic signals
Solution Approach 1:
The patent extracts specific features from the composite bioelectrical signal that are characteristic of correct catheter positioning. By identifying and isolating particular ECG waveform characteristics (such as amplitude changes, morphology variations, or specific interval changes) that occur when the catheter tip is near the diaphragm, the system separates the positioning information from the dominant ECG background.
Solution Approach 2:
The patent changes the parameters used for signal analysis from standard ECG interpretation to specific parameters that indicate diaphragmatic proximity. This includes analyzing changes in signal amplitude, frequency content, or waveform morphology that occur when the catheter approaches the diaphragm, transforming the dominant ECG signal into a useful positioning indicator.
3Device complexity
If manual monitoring by clinician is used to assess catheter position, then system complexity is reduced, but productivity decreases due to reliance on continued clinician attention
Solution Approach 1:
The patent implements a self-monitoring system where the catheter itself, equipped with integrated sensors and processing electronics, automatically detects and communicates its position relative to the diaphragm. The system performs self-diagnosis and provides real-time feedback without requiring external clinician intervention, enabling continuous autonomous monitoring.
Solution Approach 2:
The patent incorporates a feedback mechanism that continuously monitors bioelectrical signals and provides real-time information about catheter positioning status. The system compares detected signal characteristics against reference values and alerts clinicians or automatically adjusts the catheter position, creating a closed-loop control system that improves efficiency while maintaining manageable complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of catheter positioning by providing granular classification and reducing reliance on clinician attention, minimizing complications and ensuring reliable bioelectrical signal availability for connected devices.
Implementation Method 1
The sensors can measure electrocardiographic signals and electromyographic signals emanating from nearby muscles within the body
Implementation Method 2
The sensors can measure electrocardiographic signals and electromyographic signals emanating from nearby muscles within the body
Data Source
AI summary
The present disclosure relates to position monitoring of medical devices, and more specifically to technologies for enabling the automatic monitoring of a position of a catheter in relation to a diaphragm. Aspects of the disclosure comprises determining training data to be used for training a machine learning algorithm to classify a position of a catheter in relation to a diaphragm of a patient. Further aspects of the disclosure comprising using a trained machine learning algorithm for classifying a position of a catheter in relation to a diaphragm of a patient.