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

Abstract

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.

AI Sound Recognition on Asthma Medication Adherence: Evaluation With the RDA Benchmark Suite

“…Furthermore, an other interesting application of SVM for respiratory signal classification is presented in Eleftheriadou et al [107], where an audio-based method is assessing the proper usage of dry powder inhalers, by using the FFT of the signal. A window size of 512 frames, with a number of frames between FFT columns (128 frames), was used and 16 MFCC's including the zero coefficient, were returned resulting in an array of size (16 × 126).…”

“…Recent work on inhalation signal classification [107] has used Gradient Boosting, as a numerical optimization method. The objective has been to minimize the loss function by adding weak learners in a gradient descent type procedure.…”