TY - GEN
T1 - EEG-based Analysis Study for Patients Receiving Intravenous Antibiotic Medication
AU - Chkirbene, Zina
AU - Al-Marridi, Abeer Z.
AU - Abdellatif, Alaa Awad
AU - Mohamed, Amr
AU - Erbad, Aiman
AU - O'Connor, Mark Dennis
AU - Laughton, James
AU - Villacorte, Anthony
AU - Menez, Johansen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
KW - Deep learning
KW - EEG data
KW - epileptic seizure
UR - https://www.scopus.com/pages/publications/85089671261
U2 - 10.1109/IWCMC48107.2020.9148063
DO - 10.1109/IWCMC48107.2020.9148063
M3 - Conference contribution
AN - SCOPUS:85089671261
T3 - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
SP - 1897
EP - 1902
BT - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Y2 - 15 June 2020 through 19 June 2020
ER -