Ventricular suction detection algorithm designed for ventricular assist devices

Abstract

Background: Ventricular assist devices (VADs) are an effective treatment for end-stage heart failure and can significantly improve patients’ quality of life. However, when the rotational speed of the VAD does not match the intraventricular blood volume, ventricular suction may occur. Severe suction can lead to ventricular collapse, making accurate and real-time suction detection critically important.
Methods: Two statistical features and two frequency-domain features were extracted from the pump flow signal to build a classification and regression tree (CART) model. Additionally, a secondary decision-making process was applied using a time-domain threshold.
Results: The proposed method was validated using both in vivo and in vitro experimental data. Experimental results show that, compared to existing suction detection techniques, the proposed approach not only reduces computational complexity but also achieves higher detection accuracy and enhanced algorithmic stability.
Conclusions: The proposed method provides a more efficient and reliable solution for real-time ventricular suction detection, which is crucial for the safe operation of VADs in clinical settings.