Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest

Owen Ma; Arindam Dutta; Daniel Bliss; Amy Z. Crepeau

doi:10.1109/ACSSC.2017.8335566

Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest

Owen Ma, Arindam Dutta, Daniel Bliss, Amy Z. Crepeau

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1313-1317
Number of pages	5
ISBN (Electronic)	9781538618233
DOIs	https://doi.org/10.1109/ACSSC.2017.8335566
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

Publication series

Name	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume	2017-October

Other

Other	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Country/Territory	United States
City	Pacific Grove
Period	10/29/17 → 11/1/17

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

Access to Document

10.1109/ACSSC.2017.8335566

Cite this

Ma, O., Dutta, A., Bliss, D., & Crepeau, A. Z. (2018). Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (pp. 1313-1317). Article 8335566 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335566

Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest. / Ma, Owen; Dutta, Arindam; Bliss, Daniel et al.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1313-1317 8335566 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ma, O, Dutta, A, Bliss, D & Crepeau, AZ 2018, Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest. in MB Matthews (ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017., 8335566, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 1313-1317, 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Pacific Grove, United States, 10/29/17. https://doi.org/10.1109/ACSSC.2017.8335566

Ma O, Dutta A, Bliss D, Crepeau AZ. Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest. In Matthews MB, editor, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1313-1317. 8335566. (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017). doi: 10.1109/ACSSC.2017.8335566

Ma, Owen ; Dutta, Arindam ; Bliss, Daniel et al. / Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. editor / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1313-1317 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017).

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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.",

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author = "Owen Ma and Arindam Dutta and Daniel Bliss and Crepeau, {Amy Z.}",

note = "Funding Information: This work was sponsored by the ASU Mayo Seed Grant Program. The authors would like to thank Alex Chiriyath, Christian Chapman, Andrew Herschfelt, Bryan Paul, and Sha-ranya Srinivas of BLISS Lab for their help in revising this article. They would also like to thank Gene Brewer and Visar Berisha of Arizona State University for their feedback on this study{\textquoteright}s methodology. Additionally, they would like to thank Yonas E. Geda, Gregory A. Worrell, and William J. Mauermann of Mayo Clinic for their initial analysis on this data. Lastly, they would like to thank Daniel Crepeau of Mayo Clinic for his technical assistance with transferring and accessing the data. Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 ; Conference date: 29-10-2017 Through 01-11-2017",

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Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest

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