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Segment-Based Spotting of Bowel Sounds Using Pretrained Models in Continuous Data Streams.

Annalisa Baronetto, Luisa S Graf, Sarah Fischer

    IEEE Journal of Biomedical and Health Informatics
    |May 8, 2023
    PubMed
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    Deep neural models effectively detect bowel sounds (BS) in continuous audio, with pretrained models improving accuracy and noise robustness. This automated BS spotting significantly reduces expert review time by approximately 87%.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Healthcare
    • Signal Processing

    Background:

    • Continuous audio monitoring for bowel sounds (BS) is crucial for patient assessment.
    • Manual review of extensive audio data is time-consuming and labor-intensive.
    • Developing automated methods for BS detection is essential for clinical efficiency.

    Purpose of the Study:

    • To evaluate deep neural network models for detecting 10-second bowel sound (BS) audio segments.
    • To compare the performance of pretrained versus non-pretrained models in BS spotting.
    • To assess the feasibility of automated BS detection in semi-naturalistic settings.

    Main Methods:

    • Analysis of MobileNet, EfficientNet, and Distilled Transformer architectures for BS detection.
    • Transfer learning from AudioSet followed by evaluation on 84 hours of labelled participant audio data.
    • Utilizing smart shirts with embedded microphones for data collection in a noisy, semi-naturalistic environment.

    Main Results:

    • The best performing model, EfficientNet-B2 with an attention module, achieved a 73% F1 score for segment-based BS spotting.
    • Pretrained models improved the F1 score by up to 26% compared to non-pretrained models.
    • Automated BS spotting reduced the required expert review time from 84 hours to approximately 11 hours (an 87% reduction).

    Conclusions:

    • Pretrained deep learning models significantly enhance the accuracy and robustness of automated bowel sound detection.
    • The developed segment-based BS spotting approach offers a substantial reduction in expert workload.
    • This technology holds promise for more efficient and scalable patient monitoring through audio analysis.