Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of Adherence
Inhalers are devices which deliver medication to the airways in the treatment of chronic respiratory diseases. When used correctly inhalers relieve and improve patients' symptoms. However, adherence to inhaler medication has been demonstrated to be poor, leading to reduced clinical outcomes, wa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2014-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/6762909/ |
| _version_ | 1827340964577935360 |
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| author | Martin S. Holmes Shona D'arcy Richard W. Costello Richard B. Reilly |
| author_facet | Martin S. Holmes Shona D'arcy Richard W. Costello Richard B. Reilly |
| author_sort | Martin S. Holmes |
| collection | DOAJ |
| description | Inhalers are devices which deliver medication to the airways in the treatment of chronic respiratory diseases. When used correctly inhalers relieve and improve patients' symptoms. However, adherence to inhaler medication has been demonstrated to be poor, leading to reduced clinical outcomes, wasted medication, and higher healthcare costs. There is a clinical need for a system that can accurately monitor inhaler adherence as currently no method exists to evaluate how patients use their inhalers between clinic visits. This paper presents a method of automatically evaluating inhaler adherence through acoustic analysis of inhaler sounds. An acoustic monitoring device was employed to record the sounds patients produce while using a Diskus dry powder inhaler, in addition to the time and date patients use the inhaler. An algorithm was designed and developed to automatically detect inhaler events from the audio signals and provide feedback regarding patient adherence. The algorithm was evaluated on 407 audio files obtained from 12 community dwelling asthmatic patients. Results of the automatic classification were compared against two expert human raters. For patient data for whom the human raters Cohen's kappa agreement score was <formula formulatype="inline"><tex Notation="TeX">${>}{0.81}$</tex></formula>, results indicated that the algorithm's accuracy was 83% in determining the correct inhaler technique score compared with the raters. This paper has several clinical implications as it demonstrates the feasibility of using acoustics to objectively monitor patient inhaler adherence and provide real-time personalized medical care for a chronic respiratory illness. |
| first_indexed | 2024-03-07T21:30:08Z |
| format | Article |
| id | doaj.art-c26f91afffd2444d8343cbdcc01520de |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-03-07T21:30:08Z |
| publishDate | 2014-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-c26f91afffd2444d8343cbdcc01520de2024-02-27T00:00:12ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722014-01-01211010.1109/JTEHM.2014.23104806762909Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of AdherenceMartin S. Holmes0Shona D'arcy1Richard W. Costello2Richard B. Reilly3Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, IrelandTrinity Centre for Bioengineering, Trinity College Dublin, Dublin, IrelandRoyal College of Surgeons in Ireland, Pulmonary Function Unit, Beaumont Hospital, Dublin, IrelandTrinity Centre for Bioengineering, Trinity College Dublin, Dublin, IrelandInhalers are devices which deliver medication to the airways in the treatment of chronic respiratory diseases. When used correctly inhalers relieve and improve patients' symptoms. However, adherence to inhaler medication has been demonstrated to be poor, leading to reduced clinical outcomes, wasted medication, and higher healthcare costs. There is a clinical need for a system that can accurately monitor inhaler adherence as currently no method exists to evaluate how patients use their inhalers between clinic visits. This paper presents a method of automatically evaluating inhaler adherence through acoustic analysis of inhaler sounds. An acoustic monitoring device was employed to record the sounds patients produce while using a Diskus dry powder inhaler, in addition to the time and date patients use the inhaler. An algorithm was designed and developed to automatically detect inhaler events from the audio signals and provide feedback regarding patient adherence. The algorithm was evaluated on 407 audio files obtained from 12 community dwelling asthmatic patients. Results of the automatic classification were compared against two expert human raters. For patient data for whom the human raters Cohen's kappa agreement score was <formula formulatype="inline"><tex Notation="TeX">${>}{0.81}$</tex></formula>, results indicated that the algorithm's accuracy was 83% in determining the correct inhaler technique score compared with the raters. This paper has several clinical implications as it demonstrates the feasibility of using acoustics to objectively monitor patient inhaler adherence and provide real-time personalized medical care for a chronic respiratory illness.https://ieeexplore.ieee.org/document/6762909/Acousticsadherencealgorithmchronic respiratory diseasesinhaler |
| spellingShingle | Martin S. Holmes Shona D'arcy Richard W. Costello Richard B. Reilly Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of Adherence IEEE Journal of Translational Engineering in Health and Medicine Acoustics adherence algorithm chronic respiratory diseases inhaler |
| title | Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of Adherence |
| title_full | Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of Adherence |
| title_fullStr | Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of Adherence |
| title_full_unstemmed | Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of Adherence |
| title_short | Acoustic Analysis of Inhaler Sounds From Community-Dwelling Asthmatic Patients for Automatic Assessment of Adherence |
| title_sort | acoustic analysis of inhaler sounds from community dwelling asthmatic patients for automatic assessment of adherence |
| topic | Acoustics adherence algorithm chronic respiratory diseases inhaler |
| url | https://ieeexplore.ieee.org/document/6762909/ |
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