Respiratory and Drug Actuation Dataset

Asthma is a common, usually long-term respiratory disease with negative impact on society and the economy worldwide. Treatment involves using medical devices (inhalers) that distribute medicationto the airways, and its efficiency depends on the precision of the inhalation technique. Health monitoring systems equipped with sensors and embedded with sound signal detection enable the recognition of drug actuation and could be powerful tools for reliable audio content analysis. The RDA Suite includes a set of tools for audio processing, feature extraction and classification and is provided along with a dataset consisting of respiratory and drug actuation sounds. The classification models in RDA are implemented based on conventional and advanced machine learning and deep network architectures. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses challenges and future tendencies. The central aim of this research is to identify associations between high-level classification labels and low-level features extracted  from  audio  clips  of  different  semantic  activities. We  investigate  the  clinical  applicability  of  different  audio-based  signal  processing  methods  for  assessing  medication adherence. The dataset consists of recordings acquired in anacoustically controlled setting, free of ambient indoor environment  noise,  at  the  University  of  Patras.  Three  subjects, who  were  familiar  with  the  inhaler  technique,  participated in  the  study.  The  participants  were  instructed  to  use  the inhaler, as  typically performed in  a clinical procedure. Foreach and every participant informed consent was obtained.During breathing and drug actuation, the audio signals were acquired  by  a  microphone  attached  to  the  inhalation  de-vice,  communicating  with  a  mobile  phone  via  Bluetooth.The  addition  of  the  adherence  monitoring  device  did  not impact  the  normal  functioning  of  the  inhaler,  which  had  afull placebo canister. In total, 370 audio files were recorded with a different duration each, containing an entire inhaleruse  case,  with  respiratory  flow  ranging  on  180-240  L/min.Each  audio  recording  was  sampled  with  a  8KHz  sampling frequency,  as  a  mono  channel  WAV  file,  at  8-bit  depth.The  audio  recordings  were  segmented  and  annotated  by  a human specialist into inhaler actuation, exhalation, inhalationand  environmental  noise.  The  obtained  segments  (of  non-mixed states) were of variable length and, for some methods, were further segmented into frames of fixed length for the purposes of feature extraction.The constructed database overall consisted of 193 drug actuation segments, 319 inhalation and 620 exhalation segments and 505 noise segments, ready to be used for audio sound recognition using different sets of features.