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.