A Nasal Resistance Measurement System Based on Multi-Sensor Fusion of Pressure and Flow

Area of Science:

• Biomedical Engineering
• Otolaryngology

Background:

• Nasal obstruction is a common symptom requiring accurate severity assessment.
• Traditional rhinomanometry suffers from interference, limited automation, and unstable results.

Purpose of the Study:

• To develop an automated nasal resistance measurement system using multi-sensor fusion.
• To enhance the accuracy and stability of nasal resistance measurements.

Main Methods:

• Designed a system with hardware for raw signal acquisition and software for signal processing.
• Employed multi-sensor fusion of pressure and flow data.
• Assessed anti-interference and stability using respiratory cycle recognition and standard deviation analysis.

Main Results:

• Achieved 99% recall and 86% precision in effective respiratory cycle recognition.
• Demonstrated a maximum accuracy of 96.30% in identifying ineffective respiratory cycles under interference.
• Reduced measurement result variability by 5 to 18 times compared to unfiltered methods.

Conclusions:

• The developed system offers strong anti-interference capabilities and enhanced automation.
• It significantly improves the stability of nasal resistance measurement results.
• Provides robust technical support for the auxiliary diagnosis of nasal conditions.