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An open-source framework for synthetic post-dive Doppler ultrasound audio generation
David Q Le1, Andrew H Hoang1, Arian Azarang1
1Joint Department of Biomedical Engineering, North Carolina State University, and The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Plos One
|April 27, 2023
Summary
Researchers developed a novel method to create synthetic Doppler ultrasound data for evaluating venous gas emboli (VGE). This reproducible technique aids in advancing automated VGE detection algorithms for decompression sickness.
Area of Science:
- Biomedical Engineering
- Ultrasound Technology
- Physiology
Background:
- Doppler ultrasound (DU) is crucial for detecting venous gas emboli (VGE) post-decompression.
- Current automated VGE assessment methods lack objective evaluation due to limited real-world datasets and absence of ground truth.
- Developing robust signal processing techniques for VGE analysis is essential for diving safety and hyperbaric medicine.
Purpose of the Study:
- To develop and validate a reproducible method for generating synthetic Doppler ultrasound (DU) data simulating post-decompression venous gas emboli (VGE).
- To provide researchers with a tool to create tunable datasets for improving automated VGE detection algorithms.
- To accelerate the advancement of signal processing techniques for VGE analysis in clinical and research settings.
Main Methods:
- Collected baseline DU signals from precordial and subclavian veins.
- Developed a synthetic data generation method incorporating varying degrees of bubbling, aligned with field-standard grading metrics (Spencer, Kisman-Masurel).
- Provided baseline recordings and code for data generation, alongside pre-made synthetic datasets for six distinct scenarios.
Main Results:
- Successfully generated adaptable, modifiable, and reproducible synthetic DU data representing VGE presence.
- Created datasets covering Spencer and Kisman-Masurel grading scales for both precordial and subclavian venous recordings.
- Established a foundation for objective evaluation and comparison of VGE signal processing methodologies.
Conclusions:
- The developed synthetic data generation method offers a valuable resource for researchers in the field of VGE analysis.
- This approach facilitates the objective evaluation and enhancement of automated signal processing techniques for VGE detection.
- Accelerated development of reliable VGE assessment tools can improve safety in diving and hyperbaric medicine.
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