IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v276y2019i1d10.1007_s10479-018-2823-1.html
   My bibliography  Save this article

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

Author

Listed:
  • Anahita Khojandi

    (University of Tennessee)

  • Oleg Shylo

    (University of Tennessee)

  • Maryam Zokaeinikoo

    (The Pennsylvania State University)

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.

Suggested Citation

  • Anahita Khojandi & Oleg Shylo & Maryam Zokaeinikoo, 2019. "Automatic EEG classification: a path to smart and connected sleep interventions," Annals of Operations Research, Springer, vol. 276(1), pages 169-190, May.
    • Handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-018-2823-1
    DOI: 10.1007/s10479-018-2823-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-2823-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-018-2823-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2004. "The Optimal Timing of Living-Donor Liver Transplantation," Management Science, INFORMS, vol. 50(10), pages 1420-1430, October.
    2. J. Allan Hobson, 2005. "Sleep is of the brain, by the brain and for the brain," Nature, Nature, vol. 437(7063), pages 1254-1256, October.
    3. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luigi Bianchi & Chiara Liti & Giampaolo Liuzzi & Veronica Piccialli & Cecilia Salvatore, 2022. "Improving P300 Speller performance by means of optimization and machine learning," Annals of Operations Research, Springer, vol. 312(2), pages 1221-1259, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mason, J.E. & Denton, B.T. & Shah, N.D. & Smith, S.A., 2014. "Optimizing the simultaneous management of blood pressure and cholesterol for type 2 diabetes patients," European Journal of Operational Research, Elsevier, vol. 233(3), pages 727-738.
    2. Oguzhan Alagoz & Jagpreet Chhatwal & Elizabeth S. Burnside, 2013. "Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis," Decision Analysis, INFORMS, vol. 10(3), pages 200-224, September.
    3. M. Reza Skandari & Steven M. Shechter & Nadia Zalunardo, 2015. "Optimal Vascular Access Choice for Patients on Hemodialysis," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 608-619, October.
    4. Hessam Bavafa & Sergei Savin & Christian Terwiesch, 2021. "Customizing Primary Care Delivery Using E‐Visits," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4306-4327, November.
    5. Zlatana Nenova & Jennifer Shang, 2022. "Personalized Chronic Disease Follow‐Up Appointments: Risk‐Stratified Care Through Big Data," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 583-606, February.
    6. Ting-Yu Ho & Shan Liu & Zelda B. Zabinsky, 2019. "A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management," Health Care Management Science, Springer, vol. 22(4), pages 727-755, December.
    7. Kang, Yuncheol & Sawyer, Amy M. & Griffin, Paul M. & Prabhu, Vittaldas V., 2016. "Modelling adherence behaviour for the treatment of obstructive sleep apnoea," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1005-1013.
    8. Sarang Deo & Seyed Iravani & Tingting Jiang & Karen Smilowitz & Stephen Samuelson, 2013. "Improving Health Outcomes Through Better Capacity Allocation in a Community-Based Chronic Care Model," Operations Research, INFORMS, vol. 61(6), pages 1277-1294, December.
    9. Naumzik, Christof & Feuerriegel, Stefan & Nielsen, Anne Molgaard, 2023. "Data-driven dynamic treatment planning for chronic diseases," European Journal of Operational Research, Elsevier, vol. 305(2), pages 853-867.
    10. Lauren E. Cipriano & Thomas A. Weber, 2018. "Population-level intervention and information collection in dynamic healthcare policy," Health Care Management Science, Springer, vol. 21(4), pages 604-631, December.
    11. Anthony Bonifonte & Turgay Ayer & Benjamin Haaland, 2022. "An Analytics Approach to Guide Randomized Controlled Trials in Hypertension Management," Management Science, INFORMS, vol. 68(9), pages 6634-6647, September.
    12. Wenjuan Fan & Yang Zong & Subodha Kumar, 2022. "Optimal treatment of chronic kidney disease with uncertainty in obtaining a transplantable kidney: an MDP based approach," Annals of Operations Research, Springer, vol. 316(1), pages 269-302, September.
    13. M. Reza Skandari & Steven M. Shechter, 2021. "Patient-Type Bayes-Adaptive Treatment Plans," Operations Research, INFORMS, vol. 69(2), pages 574-598, March.
    14. Chris P. Lee & Glenn M. Chertow & Stefanos A. Zenios, 2008. "Optimal Initiation and Management of Dialysis Therapy," Operations Research, INFORMS, vol. 56(6), pages 1428-1449, December.
    15. Jonathan E. Helm & Mariel S. Lavieri & Mark P. Van Oyen & Joshua D. Stein & David C. Musch, 2015. "Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support," Operations Research, INFORMS, vol. 63(5), pages 979-999, October.
    16. Mabel C. Chou & Mahmut Parlar & Yun Zhou, 2017. "Optimal Timing to Initiate Medical Treatment for a Disease Evolving as a Semi-Markov Process," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 194-217, October.
    17. Klein, Michael G. & Verter, Vedat & Moses, Brian G., 2020. "Designing a rural network of dialysis facilities," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1088-1100.
    18. Kotas, Jakob & Ghate, Archis, 2018. "Bayesian learning of dose–response parameters from a cohort under response-guided dosing," European Journal of Operational Research, Elsevier, vol. 265(1), pages 328-343.
    19. Muge Capan & Julie S. Ivy & James R. Wilson & Jeanne M. Huddleston, 2017. "A stochastic model of acute-care decisions based on patient and provider heterogeneity," Health Care Management Science, Springer, vol. 20(2), pages 187-206, June.
    20. Satır, Benhür & Erenay, Fatih Safa & Bookbinder, James H., 2018. "Shipment consolidation with two demand classes: Rationing the dispatch capacity," European Journal of Operational Research, Elsevier, vol. 270(1), pages 171-184.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-018-2823-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.