@inproceedings{575adc84beff4c75835b345d7fe115e3,
title = "Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest",
abstract = "Cardiac surgeries involving deep hypothermia circulatory arrest present a risk of cognitive impairment. This study attempts to uncover intraoperative electroencephalogram (EEG) biomarkers predictive of postoperative delirium, which is associated with long term health complications. We predict postoperative delirium diagnoses by examining changes in ensemble neural activity during surgeries through spatiotemporal eigenspectra extracted from patient EEG data. Artifact detection and feature normalization schemes are developed to facilitate this. At most 14 of 16 cases were correctly predicted with a p-value of 0.0015. An area under the receiver operating characteristics (ROC) curve of 0.8364 was achieved-0.9091 when considering the convex hull.",
keywords = "Deep Hypothermia Circulatory Arrest, Electroencephalography, Intra-operative Monitoring, Neurophysiology, Signal Processing",
author = "Owen Ma and Arindam Dutta and Bliss, {Daniel W.} and Crepeau, {Amy Z.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 ; Conference date: 29-10-2017 Through 01-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ACSSC.2017.8335566",
language = "English (US)",
series = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1313--1317",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",
}