Catheter Position Classification Using Diaphragm Bioelectrical Signals
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Solution Overview
Problem
Existing methods for accurately assessing the positioning of nasogastric catheters, particularly in sedated patients, are hindered by the dominance of electrocardiogram signals over electromyographic signals, leading to difficulties in precise catheter placement and reliance on continuous clinician attention.
Innovation Solution
A machine learning algorithm trained using bioelectrical signals from a catheter, divided into subsets based on their relative position to the diaphragm, to classify correct and incorrect positions, with techniques to balance training data and reduce reliance on electromyographic signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electromyographic signals are used to assess catheter positioning, then positioning accuracy is improved, but signal detection becomes difficult when patients are sedated and not breathing spontaneously
Solution Approach 1:
The patent introduces an intermediary approach by using electrocardiogram signals as a mediator when electromyographic signals are unavailable. The machine learning algorithm is trained to recognize catheter positioning patterns through ECG signals, which can be detected even in sedated patients who are not breathing spontaneously, thus bridging the gap when primary EMG signals fail.
Solution Approach 2:
The system dynamically changes the parameter used for assessment based on signal availability. When EMG signals are present, the system uses them for high-precision positioning. When EMG signals are absent (in sedated patients), the system switches to using ECG signals, changing the detection parameter from muscle activity to cardiac activity patterns.
2Reliability
If electrocardiogram signals are used for catheter positioning assessment, then signal availability is improved, but positioning precision deteriorates due to ECG signals being 1000 times stronger than electromyographic signals
Solution Approach 1:
The machine learning algorithm extracts the specific positioning-related information from the dominant ECG signals by training on labeled data that identifies characteristic patterns. The algorithm learns to isolate and extract the subtle positioning indicators embedded within the strong ECG signal background, effectively separating the useful positioning information from the overwhelming cardiac signals.
Solution Approach 2:
The system performs preliminary training action by pre-training the machine learning algorithm with labeled ECG signals from correctly and incorrectly positioned catheters. This preliminary training enables the algorithm to automatically recognize positioning patterns without requiring real-time signal filtering or manual intervention, allowing direct use of ECG signals for positioning assessment.
3Adaptability or versatility
If manual monitoring by clinicians is used, then flexibility in assessment is maintained, but continuous attention and time consumption increase
Solution Approach 1:
The system implements self-service by enabling automatic monitoring where the machine learning algorithm continuously analyzes bioelectrical signals and determines catheter positioning without requiring continuous clinician attention. The system serves itself by autonomously detecting signal patterns, classifying positioning status, and alerting clinicians only when intervention is needed, thereby eliminating the need for constant manual monitoring while preserving clinical judgment.
4Extent of automation
If machine learning algorithms are implemented for automatic monitoring, then continuous monitoring capability is improved, but system complexity increases
Solution Approach 1:
The patent merges the machine learning algorithm with the existing bioelectrical signal monitoring system. The algorithm is integrated into the same hardware platform that already captures ECG and EMG signals, combining the automation intelligence with the existing sensing infrastructure. This merging approach enables automatic monitoring without requiring entirely separate complex systems, as the ML model processes signals from the existing electrode array.
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
Enables continuous, automatic monitoring of catheter position, reducing the risk of complications and improving accuracy in catheter placement, independent of clinician attention and minimizing the need for additional verification methods.
Implementation Method 1
The sensors can measure electrocardiogram signals and electromyographic signals emanating from nearby muscles within the body
Implementation Method 2
The sensors can measure electrocardiogram 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.