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

  • S.I.: Computational Biomedicine
  • Published:
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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.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, USA

    Anahita Khojandi & Oleg Shylo

  2. Harold And Inge Marcus Department of Industrial And Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA

    Maryam Zokaeinikoo

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  1. Anahita Khojandi
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  2. Oleg Shylo
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Correspondence to Oleg Shylo.

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Khojandi, A., Shylo, O. & Zokaeinikoo, M. Automatic EEG classification: a path to smart and connected sleep interventions. Ann Oper Res 276, 169–190 (2019). https://doi.org/10.1007/s10479-018-2823-1

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  • DOI: https://doi.org/10.1007/s10479-018-2823-1

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