Predictive Maintenance Approach in Ventricular Assist Devices: Safeguarding Against Thrombus Formation

Abstract

Affecting millions in the world, cardiovascular diseases are a public health problem. Some patients are not eligible for heart transplantation. Thus, a possibility is to receive a circulatory device known as a ventricular assist device (VAD). This kind of device shows some problems, like thrombogenesis. The thrombus formation in a VAD can cause patient death, and a previous, non-invasive diagnostic is quite complex. The objective of this work is to develop an algorithm to reproduce time signals that indicate the presence and absence of a thrombus, use these signals to train an artificial neural network to classify them, and use these algorithms in a predictive algorithm for early thrombus detection. The results show that it was possible to detect the thrombus formation in its early stages, but the noise level interferes with the accuracy of the ANN, especially when signals in the time domain are used.