Emboli are solid or gaseous materials that circulate in the bloodstream until they become lodged in a blood vessel. The presence of emboli is related to the risk of strokes and can cause body damage at several levels.
This work addresses the detection of emboli from signals acquired with a new miniaturized and portable transcranial Doppler ultrasound device. The use of this device enables outpatient monitoring but increases the number of artifacts. These artifacts usually come from the patient voice and motion and can be superimposed to emboli. For this reason and because of the scarcity of emboli compared to artifacts, reliably detect emboli is a challenging task. As an example, the 11809 s of signal used in this study contained 0.06 % of embolic events and 10.14 % of artifacts. Herein, we propose an automatic and sequential approach. The method is based on sequential determination of high intensity transient signal. We also define efficient features to describe emboli in the time frequency representation. On our database, the number of artifacts detected as emboli is divided by more than 10 compared to the other algorithms reported in the literature. This pipeline (Figure) is implemented on the new, miniaturized, mono-gate and portable TCD-X device (Atys Medical, France) since 2018. This work is part of ANR Labcom AtysCrea (2014-2017) and was published in journals [Guépié-2017-Medical and Biological Engineering
and Computing] and [Guépié-2018-IEEE Journal of Biomedical and Health Informatics].
Fig. Framework of the emboli detection process. The first part achieves HITS detection in the time domain. The second part concerns the features extraction from the HIT region and the ROI. The third part provides the HITS classification.
This work continues as part of a AURA project 2019-2024.