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Updated: Feb 20, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Phenotype classification using the combination of lung sound analysis and fractional exhaled nitric oxide for
Terufumi Shimoda1, Yasushi Obase2, Yukio Nagasaka3
1San Remo Rehabilitation Hospital, Sasebo, Japan; Clinical Research Center, Fukuoka National Hospital, Fukuoka, Japan.
Background:
We report the utility of combining lung sound analysis and fractional exhaled nitric oxide (FeNO) for phenotype classification of airway inflammation in patients with bronchial asthma. We investigated the usefulness of the combination of the expiration-to-inspiration sound power ratio in the mid-frequency range (E/I MF) of 200-400 Hz and FeNO for comprehensively classifying disease type and evaluating asthma treatment.
Methods:
A total of 233 patients with bronchial asthma were included. The cutoff values of FeNO and E/I MF were set to 38 ppb and 0.36, respectively, according to a previous study. The patients were divided into 4 subgroups based on the FeNO and E/I MF cutoff values. Respiratory function, the percentages of sputum eosinophils and neutrophils, and patient background characteristics were compared among groups.
Results:
Respiratory function was well controlled in the FeNO low/E/I MF low group (good control). Sputum neutrophil was higher and FEV1,%pred was lower in the FeNO low/E/I MF high group (poor control). History of childhood asthma and atopic asthma were associated with the FeNO high/E/I MF low group (insufficient control). The FeNO high/E/I MF high group corresponded to a longer disease duration, increased blood or sputum eosinophils, and lower FEV1/FVC (poor control).
Conclusions:
The combination of FeNO and E/I MF assessed by lung sound analysis allows the condition of airway narrowing and the degree of airway inflammation to be assessed in patients with asthma and is useful for evaluating bronchial asthma treatments.
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