Year: 2026
Type: article
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: 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.
Source: Frontiers in Medical Technology
Authors: Yijiao Wu, Yuzhuo Yang, Xudong Pan, Shunzhou Yu
Institution: Harbin Institute of Technology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7123351327