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Ventricular suction detection algorithm designed for ventricular assist devices
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DOI：10.3389/fmedt.2025.1748577.png)
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Abstract
En 中文
BackgroundVentricular 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.MethodsTwo 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.ResultsThe 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.ConclusionsThe 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.
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F
IF:
3.8
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