Abstract
Effective management of chronic constrictive pulmonary conditions lies in proper and timely administration of medication. As a series of studies indicates, medication adherence can effectively be monitored by successfully identifying actions performed by patients during inhaler usage. This study focuses on the recognition of inhaler audio events during usage of pressurized metered dose inhalers (pMDI). Aiming at real-time performance, we investigate deep sparse coding techniques including convolutional filter pruning, scalar pruning and vector quantization, for different convolutional neural network (CNN) architectures. The recognition performance has been assessed on three healthy subjects following both within and across subjects modeling strategies. The selected CNN architecture classified drug actuation, inhalation and exhalation events, with 100%, 92.6% and 97.9% accuracy, respectively, when assessed in a leave-one-subject-out cross-validation setting. Moreover, sparse coding of the same architecture with an increasing compression rate from 1 to 7 resulted in only a small decrease in classification accuracy (from 95.7% to 94.5%), obtained by random (subject-agnostic) cross-validation. A more thorough assessment on a larger dataset, including recordings of subjects with multiple respiratory disease manifestations, is still required in order to better evaluate the method’s generalization ability and robustness.
Highlights
The respiratory system is a vital structure vulnerable to airborne infection and injury.Respiratory diseases are leading causes of death and disability across all ages in the world.nearly 65 million people suffer from chronic obstructive pulmonary disease (COPD) and 3 million die from it each year
Nousias et al in Reference [13] presented a comparative study between Random Forest, ADABoost, Support Vector Machines and Gaussian Mixture Models, reaching the conclusion that RF and GMM yield a 97% to 98% classification accuracy on the examined dataset, when utilizing Mel-Frequency Ceptral Coefficients (MFCCs), Spectrogram and Cepstrogram features
It outlines the need to examine the technological limitations for real time monitoring of pressurized metered dose inhalers (pMDI) usage, in order to create easy to use tools for safe and effective management
Summary
Respiratory diseases are leading causes of death and disability across all ages in the world. Nearly 65 million people suffer from chronic obstructive pulmonary disease (COPD) and 3 million die from it each year. About 334 million people suffer from asthma, the most common chronic disease of childhood, affecting 14% of all children globally [1]. The effective management of chronic constrictive pulmonary conditions lies, mainly, in the proper and timely administration of medication. As recently reported [2], a large proportion of patients use their inhalers incorrectly. Studies have shown that possible technique errors can have an adverse impact on clinical outcome for users of inhaler medication [3,4]. Incorrect inhaler usage and poor adherence were found to be associated with high COPD assessment test scores [5], short durations of COPD, high durations of hospitalization and high numbers of exacerbations
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