Copertina

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

Salvato in:
Pubblicato in:Micromachines vol. 16, no. 8 (2025), p. 886-913
Autore principale: Lian Xiaoqin
Altri autori: Ma Guochun, Gao, Chao, Liu Chunquan, Wu Yelan, Guan Wenyang
Pubblicazione:
MDPI AG
Soggetti:
Physiology
Recall
Respiratory system
Accuracy
Fluid dynamics
Cooperation
Automation
Recognition
Magnetic resonance imaging
Signal processing
Sensors
Indicators
Standard deviation
Pressure distribution
Measurement techniques
Methods
Stability
Respiration
Filtration
Multisensor fusion
Accesso online:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Abstract:Nasal obstruction is a common symptom of nasal conditions, with nasal resistance being a crucial physiological indicator for assessing severity. However, traditional rhinomanometry faces challenges with interference, limited automation, and unstable measurement results. To address these issues, this research designed a nasal resistance measurement system based on multi-sensor fusion of pressure and flow. The system comprises lower computer hardware for acquiring raw pressure–flow signals in the nasal cavity and upper computer software for segmenting and filtering effective respiratory cycles and calculating various nasal resistance indicators. Meanwhile, the system’s anti-interference capability was assessed using recall, precision, and accuracy rates for respiratory cycle recognition, while stability was evaluated by analyzing the standard deviation of nasal resistance indicators. The experimental results demonstrate that the system achieves recall and precision rates of 99% and 86%, respectively, for the recognition of effective respiratory cycles. Additionally, under the three common interference scenarios of saturated or weak breaths, breaths when not worn properly, and multiple breaths, the system can achieve a maximum accuracy of 96.30% in identifying ineffective respiratory cycles. Furthermore, compared to the measurement without filtering for effective respiratory cycles, the system reduces the median within-group standard deviation across four types of nasal resistance measurements by 5 to 18 times. In conclusion, the nasal resistance measurement system developed in this research demonstrates strong anti-interference capabilities, significantly enhances the automation of the measurement process and the stability of the measurement results, and offers robust technical support for the auxiliary diagnosis of related nasal conditions.
ISSN:2072-666X
DOI:10.3390/mi16080886
Fonte:Engineering Database