A New Method to Improve the Quality of Embolic Ultrasound Signal Detection

Doppler ultrasound (DU) measurements are used to detect and evaluate venous gas emboli (VGE) formed after decompression. Automated methodologies for assessing VGE presence using signal processing have been developed on varying real-world datasets of limited size and without ground truth values preventing objective evaluation. We develop and report a method to generate synthetic post-dive data using DU signals collected in both precordium and subclavian vein with varying degrees of bubbling matching field-standard grading metrics. This method is adaptable, modifiable, and reproducible, allowing for researchers to tune the produced dataset for their desired purpose. We provide the baseline Doppler recordings and code required to generate synthetic data for researchers to reproduce our work and improve upon it. We also provide a set of pre-made synthetic post-dive DU data spanning six scenarios representing the Spencer and Kisman-Masurel (KM) grading scales as well as precordial and subclavian DU recordings. By providing a method for synthetic post-dive DU data generation, we aim to improve and accelerate the development of signal processing techniques for VGE analysis in Doppler ultrasound.

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A New Method to Improve the Quality of Embolic Ultrasound Signal Detection

Cited by 2 publications

References 15 publications

Deep Learning-Based Venous Gas Emboli Grade Classification in Doppler Ultrasound Audio Recordings

Deep Learning-Based Venous Gas Emboli Grade Classification in Doppler Ultrasound Audio Recordings

An open-source framework for synthetic post-dive Doppler ultrasound audio generation

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