Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning

Abstract

Objectives: Cerebral edema, a prevalent consequence of brain injury, is associated with significant mortality and disability. Timely diagnosis and monitoring are crucial for patient prognosis. There is a pressing clinical demand for a real-time, non-invasive cerebral edema monitoring method. Ultrasound methods are prime candidates for such investigations due to their non-invasive nature. Methods: Acute cerebral edema was introduced in rats by permanently occluding the left middle cerebral artery (MCA). Ultrasonic echo signals were collected at nine time points over a 24 h period to extract features from both the time and frequency domains. Concurrently, histomorphological changes were examined. We utilized support vector machine (SVM), logistic regression (LogR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, linear regression (LR), and feedforward neural network (FNNs) for predicting the cerebral infarction volume ratio. Results: The integration of 16 ultrasonic features associated with cerebral edema development with the RF model enabled effective classification of cerebral edema types, with a high accuracy rate of 97.9%. Additionally, it provided an accurate prediction of the cerebral infarction volume ratio, with an R2 value of 0.8814. Conclusions: Our proposed strategy classifies cerebral edema and predicts the cerebral infarction volume ratio with satisfactory precision. The fusion of ultrasound echo features with machine learning presents a promising non-invasive approach for the monitoring of cerebral edema.