Data from: Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence

Asthma and chronic obstructive pulmonary disease (COPD) patients are
required to inhale forcefully and deeply to receive medication when using
a dry powder inhaler (DPI). There is a clinical need to objectively
monitor the inhalation flow profile of DPIs in order to remotely monitor
patient inhalation technique. Audio-based methods have been previously
employed to accurately estimate flow parameters such as the peak
inspiratory flow rate of inhalations, however, these methods required
multiple calibration inhalation audio recordings. In this study, an
audio-based method is presented that accurately estimates inhalation flow
profile using only one calibration inhalation audio recording. Twenty
healthy participants were asked to perform 15 inhalations through a
placebo Ellipta™ DPI at a range of inspiratory flow rates. Inhalation flow
signals were recorded using a pneumotachograph spirometer while inhalation
audio signals were recorded simultaneously using the Inhaler Compliance
Assessment device attached to the inhaler. The acoustic (amplitude)
envelope was estimated from each inhalation audio signal. Using only one
recording, linear and power law regression models were employed to
determine which model best described the relationship between the
inhalation acoustic envelope and flow signal. Each model was then employed
to estimate the flow signals of the remaining 14 inhalation audio
recordings. This process repeated until each of the 15 recordings were
employed to calibrate single models while testing on the remaining 14
recordings. It was observed that power law models generated the highest
average flow estimation accuracy across all participants (90.89±0.9% for
power law models and 76.63±2.38% for linear models). The method also
generated sufficient accuracy in estimating inhalation parameters such as
peak inspiratory flow rate and inspiratory capacity within the presence of
noise. Estimating inhaler inhalation flow profiles using audio based
methods may be clinically beneficial for inhaler technique training and
the remote monitoring of patient adherence.