Recognition of Breathing Activity and Medication Adherence using LSTM Neural Networks

Abstract

Obstructive inflammatory pulmonary diseases are life-long conditions of the airways affecting millions worldwide. A crucial step towards effective self-management is the adherence of the patients to their medication. Accurate detection of pressurised metered dose inhaler audio events can significantly improve medication adherence facilitating more meaningful interventions by medical personnel. Towards this direction, this work presents a data-driven approach for monitoring pressurised metered dose inhaler medication adherence employing recurrent neural networks with long short term memory (LSTM) units with spectrogram features. Evaluation studies took place with intra-subject and inter-subject settings, demonstrating that our LSTM-based approach yields higher accuracy compared to classical machine learning methods reaching a prediction accuracy ranging from 92% to 94% taking into account also samples comprised of a mixture of patterns belonging to more than one of the predefined classes.