Automatic EEG classification: a path to smart and connected sleep interventions

Anahita Khojandi, Oleg Shylo () and Maryam Zokaeinikoo
Additional contact information
Anahita Khojandi: University of Tennessee
Oleg Shylo: University of Tennessee
Maryam Zokaeinikoo: The Pennsylvania State University

Annals of Operations Research, 2019, vol. 276, issue 1, No 8, 169-190

Abstract: Abstract We develop a random forest classifier to automatically classify brain waves into sleep stages by using the publicly available data from PhysioBank. More specifically, we use the EEG signals from a single pair of electrodes (FPz–Cz) recorded from 20 patients and evaluate the impact of data balancing and incorporating signal history on classification results. The accuracy of the model is objectively evaluated using leave-one-out cross-validation. The developed model achieves the mean accuracy of 0.74, with that of the individual sleep stages ranging from 0.65 to 0.91. Next, we leverage this online sleep scoring scheme to introduce dynamic interventions as sleep process evolves over night. We develop a semi-Markov decision process model to determine optimal intervention policies to minimize the gap between the amount of sleep experienced in different stages and predetermined targets and provide computational results.

Keywords: Sleep scoring; Bio-signals; Random forest classifier; Semi-Markov decision process (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-018-2823-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-018-2823-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-018-2823-1

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().