Simulation of dynamic monitoring for intracerebral hemorrhage based on magnetic induction phase shift technology

Abstract

Intracerebral hemorrhage (ICH) is a common and severe brain disease associated with high mortality and morbidity. Accurate measurement of the ICH area is an essential indicator for doctors to determine whether a surgical operation is necessary. However, although currently used clinical detection methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), provide high-quality images, they may have limitations such as high costs, large equipment size, and radiation exposure to the human body in the case of CT. It makes long-term bedside monitoring infeasible. This paper presents a dynamic monitoring method for ICH areas based on magnetic induction. This study investigates the influence of the bleeding area and the position of ICH on the phase difference at the detection point near the area to be measured. The study applies a neural network algorithm to predict the bleeding area using the phase difference data received by the detection coil as the network input and the bleeding area as the network output. The relative error between the predicted and actual values of the neural network is calculated, and the error of each group of data is less than 4%, which confirms the feasibility of this method for detecting and even trend monitoring of the ICH area.