Spectral Tensor Breath Detection for Ventilator Dyssynchrony
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Solution Overview
Problem
Patients on mechanical ventilation face challenges in identifying pathologic breathing patterns such as patient-ventilator dyssynchrony and high work of breathing, which are difficult to recognize without specialized equipment and expertise, and often require esophageal manometry data that is not readily available, posing risks of lung injury and neurocognitive issues.
Innovation Solution
A machine learning model trained using spectral tensor techniques on flow, airway pressure, and esophageal manometry waveforms to detect pathologic breathing patterns, allowing for autonomous detection without specialized clinician training, even in pediatric populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If esophageal manometry data is used to detect pathologic breathing patterns, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent creates a computational model that copies the diagnostic capabilities of esophageal manometry by training machine learning algorithms on spectral tensor representations of respiratory waveforms. This virtual copy enables pathologic breathing detection using standard ventilator waveforms without requiring physical esophageal manometry equipment, thereby maintaining measurement precision while eliminating device complexity
Solution Approach 2:
The patent replaces the mechanical esophageal manometry system with an information-processing system. Instead of using physical pressure sensors in the esophagus, the invention uses computational algorithms that analyze spectral features of existing ventilator waveforms (flow, airway pressure) to detect pathologic breathing patterns, substituting mechanical measurement with computational analysis
2Measurement precision
If esophageal manometry is used for pathologic breathing detection, then measurement precision is improved, but ease of operation worsens due to specialized training requirements
Solution Approach 1:
The patent implements a self-service diagnostic system where the computational model automatically analyzes respiratory waveforms and detects pathologic breathing patterns without requiring clinician expertise in waveform interpretation. The system performs self-diagnosis by comparing spectral features against trained detection algorithms, eliminating the need for specialized training while maintaining detection accuracy
Solution Approach 2:
The patent introduces an intermediary computational layer between the raw waveforms and the clinician. The machine learning model acts as a mediator that translates complex spectral tensor data into actionable clinical insights, bridging the gap between sophisticated analysis and simple clinical interpretation without requiring the clinician to understand the underlying complexity
3Measurement precision
If spectral tensor techniques with machine learning are used, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model offline using spectral tensor representations of labeled respiratory data. The complex spectral analysis and model training are completed beforehand, creating a ready-to-use detection system that applies pre-learned patterns to new waveforms without requiring complex real-time computation, thus improving detection accuracy while managing device complexity
Data Source
AI summary
In some embodiments, a spectral tensor technique includes the steps of generating a power spectrogram and a phase spectrogram for the breath triplet; removing high frequency bins from each spectrogram; generating a spectral image by sizing each spectrogram to a pre-determined size; and assembling the spectral images generated for each breath triplet into the spectral tensor. Spectral tensors may be utilized as input to train a pathologic breath detection model. Spectral tensors may also be utilized by a pathologic breath detection model to analyze a new waveform that may or may not include a pathologic breath/pathologic breathing pattern.