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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Automatic Audio-Based Classification of Patient Inhaler Use: A Pharmacy Based Study.

Johnny McNulty, Richard B Reilly, Terence E Taylor

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    Poor inhaler technique hinders medication effectiveness for chronic respiratory diseases. An audio analysis algorithm accurately assesses patient adherence to Diskus dry powder inhaler use, aiding clinical monitoring.

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    Area of Science:

    • Respiratory Medicine
    • Biomedical Engineering
    • Data Science

    Background:

    • Chronic respiratory diseases require effective medication delivery via inhalers.
    • Poor adherence to correct inhaler technique limits therapeutic benefits.
    • Objective monitoring of inhaler use is crucial for patient management.

    Purpose of the Study:

    • To develop and validate an audio-based algorithm for assessing patient adherence to Diskus dry powder inhaler technique.
    • To automatically classify inhaler sounds for objective compliance monitoring.
    • To provide a tool for healthcare professionals to monitor patient inhaler treatment adherence.

    Main Methods:

    • Utilized the Inhaler Compliance Assessment device to record audio of patients using a Diskus dry powder inhaler.
    • Developed an algorithm employing audio-based signal processing and quadratic discriminant analysis (QDA).
    • Classified inhaler sounds into three categories: blister, inhalation, and interference.

    Main Results:

    • Achieved an overall accuracy of 85.35% in classifying inhaler sounds on the testing dataset.
    • Demonstrated high sensitivity for inhalation detection (89.22%) and blister detection (70%).
    • Successfully analyzed 350 audio recordings from 70 patients.

    Conclusions:

    • The developed audio classification algorithm offers an efficient and objective method for assessing inhaler adherence.
    • This technology has the potential for significant clinical impact in managing respiratory conditions.
    • Objective monitoring can help improve patient outcomes by ensuring effective medication delivery.