Literature DB >> 25571019

An acoustic method to automatically detect pressurized metered dose inhaler actuations.

Terence E Taylor, Martin S Holmes, Imran Sulaiman, Shona D'Arcy, Richard W Costello, Richard B Reilly.   

Abstract

Chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) affect over 400 million people and are incurable. The pressurized metered dose inhaler (pMDI) has been the most popular inhaler device in inhaled therapy in recent times. However the pMDIs require good coordination between inhaling and actuating the inhaler to deliver the aerosolized drug most effectively. Poor coordination can greatly reduce the amount of drug delivered to a patient and therefore reducing the control of respiratory disease symptoms. Acoustic methods have been recently employed to monitor inhaler technique quite effectively. This study employs a noninvasive acoustic method to detect actuation sounds in a portable monitoring device. A total of 158 actuation sounds were obtained from a group of healthy subjects (n=5) and subjects suffering from respiratory diseases (n=15). The developed algorithm generated an overall accuracy of 99.7% demonstrating that this method may have clinical potential to monitor pMDI actuation coordination. The informative feedback from this method may also be employed in clinical training to highlight patient actuation technique.

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Year:  2014        PMID: 25571019     DOI: 10.1109/EMBC.2014.6944651

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  Energy Efficient Monitoring of Metered Dose Inhaler Usage.

Authors:  Aris S Lalos; John Lakoumentas; Anastasios Dimas; Konstantinos Moustakas
Journal:  J Med Syst       Date:  2016-10-29       Impact factor: 4.460

2.  A protocol for a randomised clinical trial of the effect of providing feedback on inhaler technique and adherence from an electronic device in patients with poorly controlled severe asthma.

Authors:  Imran Sulaiman; Elaine Mac Hale; Martin Holmes; Cian Hughes; Shona D'Arcy; Terrence Taylor; Viliam Rapcan; Frank Doyle; Aoife Breathnach; Jansen Seheult; Desmond Murphy; Eoin Hunt; Stephen J Lane; Abhilash Sahadevan; Gloria Crispino; Greg Diette; Isabelle Killane; Richard B Reilly; Richard W Costello
Journal:  BMJ Open       Date:  2016-01-04       Impact factor: 2.692

3.  Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification.

Authors:  Vaggelis Ntalianis; Nikos Dimitris Fakotakis; Stavros Nousias; Aris S Lalos; Michael Birbas; Evangelia I Zacharaki; Konstantinos Moustakas
Journal:  Sensors (Basel)       Date:  2020-04-21       Impact factor: 3.576

4.  Objective Assessment of Patient Inhaler User Technique Using an Audio-Based Classification Approach.

Authors:  Terence E Taylor; Yaniv Zigel; Clarice Egan; Fintan Hughes; Richard W Costello; Richard B Reilly
Journal:  Sci Rep       Date:  2018-02-01       Impact factor: 4.379

  4 in total

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