Nasal Pressure Signal Analysis for Respiratory Flow Limitation Detection
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Solution Overview
Problem
Current respiratory disorder diagnosis and treatment systems face challenges in accurately detecting inspiratory flow limitation (IFL) non-invasively, with existing methods being either invasive or lacking in accuracy, and requiring complex signal processing techniques that do not match the gold-standard esophageal pressure-based approaches.
Innovation Solution
The development of systems and methods using flow rate signal representations to extract shape and time series features from inspiratory portions of respiratory flow rate signals, employing nasal cannula/pressure transducer arrangements for non-invasive IFL detection, treating it as a binary classification problem, and using feature vectors to label breaths as flow limited or not, with processors calculating metrics for IFL degree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive esophageal pressure-based approaches are used for IFL detection, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent uses nasal pressure signals as a non-invasive copy or proxy for esophageal pressure measurements. Instead of directly measuring esophageal pressure with invasive catheters, the system captures nasal pressure waveforms that contain correlated information about upper airway pressure dynamics, enabling IFL detection without invasion.
Solution Approach 2:
The patent introduces nasal pressure transducers as intermediary devices that indirectly measure upper airway pressure conditions. Rather than placing sensors directly in the esophagus, the system uses nasal pressure as an intermediate measurement point that reflects the pressure dynamics relevant to IFL, bridging the gap between non-invasive access and clinically relevant data.
2Ease of operation
If non-invasive nasal cannula methods are used for IFL detection, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary signal processing and analysis on the nasal pressure waveforms to extract features that are specifically indicative of IFL. By pre-processing the raw nasal pressure signals to identify characteristic patterns (such as flow limitation signatures in the pressure waveform), the system enhances the diagnostic value of the non-invasive measurement before presenting it for clinical interpretation.
Solution Approach 2:
The patent transforms the raw nasal pressure signal into derived parameters and features that are more sensitive to IFL detection. By changing the representation of the signal (e.g., analyzing waveform morphology, calculating derivatives, extracting spectral features), the system converts a simple non-invasive pressure measurement into a rich set of diagnostic parameters that accurately reflect upper airway dynamics.
3Measurement precision
If complex signal processing techniques are used for IFL detection, then measurement precision is improved, but device complexity deteriorates
Solution Approach 1:
The patent segments the nasal pressure signal into individual breath cycles and further into inspiratory and expiratory phases. By dividing the continuous pressure waveform into discrete, analyzable segments (breaths, then phases within breaths), the system can apply classification algorithms to manageable units, improving both accuracy and computational efficiency while reducing overall system complexity.
Solution Approach 2:
The patent extracts specific feature parameters from the nasal pressure waveforms that are most discriminative for IFL detection. Rather than analyzing the entire raw signal, the system identifies and extracts key features (such as pressure excursion characteristics, waveform shape parameters, or specific temporal features) that capture the essential information needed for binary classification, simplifying the processing while maintaining precision.
Data Source
AI summary
Automation for a system and/or method detects and/or controls treatment of inspiratory flow limitation. The system may include a flow rate sensor configured to generate a signal representing a respiratory flow rate of a patient. It may include a recording device configured to record the generated respiratory flow rate signal during a diagnosis session. It may include a computing device 7040 configured to detect a degree of inspiratory flow limitation of the patient on the recorded respiratory flow rate signal. The method may include extracting an inspiratory portion of each breath during a detection and/or monitoring session from a respiratory flow rate signal of the patient, calculating a feature vector from each inspiratory flow portion, labelling each feature vector as flow limited or not flow limited, and/or computing a metric based on the labels, the metric indicating the degree of inspiratory flow limitation of the patient during the session.