An Audio-Based Method for Assessing Proper Usage of Dry Powder Inhalers

Applied Sciences

Dimitrios Tzovaras¹  Antonios Lalas¹  Konstantinos Votis¹  Anastasios Vafeiadis¹  Athina-Chara Eleftheriadou¹ 

[1] Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece;

关键词: audio classification;    machine learning;    feature extraction;    MFCCs;    asthma;    COPD;   

DOI  :  10.3390/app10196677

来源: DOAJ

【 摘 要 】

Critical technique errors are very often performed by patients in the use of Dry Powder Inhalers (DPIs) resulting in a reduction of the clinical efficiency of such medication. Those critical errors include: pure inhalation, non-arming of the device, no exhalation before or after inhalation, and non-holding of breath for 5–10 s between inhalation and exhalation. In this work, an audio-based classification method that assesses patient DPI user technique is presented by extracting the the non-silent audio segments and categorizing them into respiratory sounds. Twenty healthy and non-healthy volunteers used the same placebo inhaler (Bretaris Genuair Inhaler) in order to evaluate the performance of the algorithm. The audio-based method achieved an F1-score of 89.87% in classifying sound events (Actuation, Inhale, Button Press, and Exhale). The significance of the algorithm lies not just on automatic classification but on a post-processing step of peak detection that resulted in an improvement of 5.58% on the F1-score, reaching 94.85%. This method can provide a clinically accurate assessment of the patient’s inhaler use without the supervision of a doctor.

【 授权许可】

Unknown