Ventilation Gas Delivery Prediction via Speech Pattern Analysis
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Solution Overview
Problem
Existing ventilation systems are less effective in providing respiratory support during speech due to the slower breathing rate and asymmetrical breathing patterns associated with speaking, which can lead to discomfort and inadequate gas delivery for respiratory patients.
Innovation Solution
A method using a machine learning model, specifically a neural network, to predict the time of inspiration based on a subject's speech pattern, allowing for timely and appropriate delivery of gas during speech, reducing the need for additional equipment and improving support during speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a ventilation system delivers therapy air based on typical breathing patterns, then respiratory support is provided effectively during normal conditions, but the system becomes less effective during speech due to slower breathing rate and asymmetrical breathing patterns
Solution Approach 1:
The ventilation system dynamically adapts its operation by detecting speech states and adjusting gas delivery parameters in real-time. The system transitions from static breathing pattern delivery to dynamic adjustment based on detected speech conditions, allowing it to respond to the asymmetrical breathing patterns that occur during speech.
Solution Approach 2:
The system changes operational parameters (gas delivery timing, flow rate, pressure) based on detected speech states. By monitoring speech-related parameters and adjusting ventilation parameters accordingly, the system maintains effectiveness during both normal breathing and speech-related breathing patterns.
2Reliability
If additional equipment is used to directly monitor breathing pattern during speech, then breathing support can be adjusted, but the complexity and burden of equipment setup increases
Solution Approach 1:
The ventilation system performs multiple functions using existing components: it delivers therapy air, monitors speech patterns, detects breathing states, and adjusts gas delivery. By making the system multi-functional, additional dedicated monitoring equipment is avoided while maintaining accurate breathing pattern detection during speech.
Solution Approach 2:
The ventilation system monitors its own operational context by detecting speech patterns and breathing states through its existing sensors and processing capabilities. This self-monitoring capability eliminates the need for separate external monitoring equipment, reducing overall system complexity.
Data Source
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AI summary
In an embodiment, a method (100) is described. The method comprises obtaining (102) an indication of a speech pattern of a subject and using (104) the indication to determine a predicted time of inspiration by the subject. A machine learning model is used for predicting the relationship between the speech pattern and a breathing pattern of the subject. The machine learning model can then be used to determine the predicted time of inspiration by the subject. The method further comprises controlling (106) delivery of gas to the subject based on the predicted time of inspiration by the subject.