A Nasal Resistance Measurement System Based on Multi-Sensor Fusion of Pressure and Flow
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| Publicado en: | Micromachines vol. 16, no. 8 (2025), p. 886-913 |
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| Autor principal: | |
| Otros Autores: | , , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-666X | ||
| 024 | 7 | |a 10.3390/mi16080886 |2 doi | |
| 035 | |a 3244047621 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231537 |2 nlm | ||
| 100 | 1 | |a Lian Xiaoqin |u School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; lianxq@th.btbu.edu.cn (X.L.); 2230601019@st.btbu.edu.cn (G.M.); 2330602060@st.btbu.edu.cn (C.L.); wuyel@th.btbu.edu.cn (Y.W.); guanwenyang@btbu.edu.cn (W.G.) | |
| 245 | 1 | |a A Nasal Resistance Measurement System Based on Multi-Sensor Fusion of Pressure and Flow | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Physiology | ||
| 653 | |a Recall | ||
| 653 | |a Respiratory system | ||
| 653 | |a Accuracy | ||
| 653 | |a Fluid dynamics | ||
| 653 | |a Cooperation | ||
| 653 | |a Automation | ||
| 653 | |a Recognition | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Signal processing | ||
| 653 | |a Sensors | ||
| 653 | |a Indicators | ||
| 653 | |a Standard deviation | ||
| 653 | |a Pressure distribution | ||
| 653 | |a Measurement techniques | ||
| 653 | |a Methods | ||
| 653 | |a Stability | ||
| 653 | |a Respiration | ||
| 653 | |a Filtration | ||
| 653 | |a Multisensor fusion | ||
| 700 | 1 | |a Ma Guochun |u School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; lianxq@th.btbu.edu.cn (X.L.); 2230601019@st.btbu.edu.cn (G.M.); 2330602060@st.btbu.edu.cn (C.L.); wuyel@th.btbu.edu.cn (Y.W.); guanwenyang@btbu.edu.cn (W.G.) | |
| 700 | 1 | |a Gao, Chao |u School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; lianxq@th.btbu.edu.cn (X.L.); 2230601019@st.btbu.edu.cn (G.M.); 2330602060@st.btbu.edu.cn (C.L.); wuyel@th.btbu.edu.cn (Y.W.); guanwenyang@btbu.edu.cn (W.G.) | |
| 700 | 1 | |a Liu Chunquan |u School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; lianxq@th.btbu.edu.cn (X.L.); 2230601019@st.btbu.edu.cn (G.M.); 2330602060@st.btbu.edu.cn (C.L.); wuyel@th.btbu.edu.cn (Y.W.); guanwenyang@btbu.edu.cn (W.G.) | |
| 700 | 1 | |a Wu Yelan |u School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; lianxq@th.btbu.edu.cn (X.L.); 2230601019@st.btbu.edu.cn (G.M.); 2330602060@st.btbu.edu.cn (C.L.); wuyel@th.btbu.edu.cn (Y.W.); guanwenyang@btbu.edu.cn (W.G.) | |
| 700 | 1 | |a Guan Wenyang |u School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; lianxq@th.btbu.edu.cn (X.L.); 2230601019@st.btbu.edu.cn (G.M.); 2330602060@st.btbu.edu.cn (C.L.); wuyel@th.btbu.edu.cn (Y.W.); guanwenyang@btbu.edu.cn (W.G.) | |
| 773 | 0 | |t Micromachines |g vol. 16, no. 8 (2025), p. 886-913 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244047621/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244047621/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244047621/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |