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.
| Original language | English (US) |
|---|---|
| Title of host publication | Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1313-1317 |
| Number of pages | 5 |
| Volume | 2017-October |
| ISBN (Electronic) | 9781538618233 |
| DOIs | |
| State | Published - Apr 10 2018 |
| Event | 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States Duration: Oct 29 2017 → Nov 1 2017 |
Other
| Other | 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 |
|---|---|
| Country | United States |
| City | Pacific Grove |
| Period | 10/29/17 → 11/1/17 |
Fingerprint
Keywords
- Deep Hypothermia Circulatory Arrest
- Electroencephalography
- Intra-operative Monitoring
- Neurophysiology
- Signal Processing
ASJC Scopus subject areas
- Control and Optimization
- Computer Networks and Communications
- Hardware and Architecture
- Signal Processing
- Biomedical Engineering
- Instrumentation
Cite this
Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest. / Ma, Owen; Dutta, Arindam; Bliss, Daniel W.; Crepeau, Amy Z.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Vol. 2017-October Institute of Electrical and Electronics Engineers Inc., 2018. p. 1313-1317 8335566.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest
AU - Ma, Owen
AU - Dutta, Arindam
AU - Bliss, Daniel W.
AU - Crepeau, Amy Z.
PY - 2018/4/10
Y1 - 2018/4/10
N2 - 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.
AB - 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.
KW - Deep Hypothermia Circulatory Arrest
KW - Electroencephalography
KW - Intra-operative Monitoring
KW - Neurophysiology
KW - Signal Processing
UR - http://www.scopus.com/inward/record.url?scp=85050966239&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050966239&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2017.8335566
DO - 10.1109/ACSSC.2017.8335566
M3 - Conference contribution
AN - SCOPUS:85050966239
VL - 2017-October
SP - 1313
EP - 1317
BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -