EEG-based Analysis Study for Patients Receiving Intravenous Antibiotic Medication

In this paper, we conduct a biological data collection and analysis study for patients undergoing routine planned intravenous antibiotic treatment. The acquired data (i.e., Electroencephalogram (EEG), temperature and blood pressure) are processed using different machine learning and deep learning models to learn the dynamic properties of brain electrical activity from this group of patients. Thus, the primary objective of our study is the safe collection of EEG data from patients receiving antibiotic therapy, in addition to analyzing the acquired data for patterns that might indicate risk of seizure. We propose two machine learning models to analyze the acquired data from these patients split into three classes: data collected before, during, and after receiving the medication. Our results show the effectiveness of our models in analyzing the acquired data, which would not possible by imitative human analysis.