Research Output
EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network
  Objective: Classification of sleep-wake states using multichannel electroencephalography (EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) neural network is developed to classify sleep-wake states using multichannel bipolar EEG signals, which takes an input vector of size 108 containing the joint features of 9 channels. The network avoids any post-processing step in order to work as a full-fledged real-time application. For training and testing the model, EEG recordings of 3525 30-second segments from 19 neonates (postmenstrual age of 37 ± 05 weeks) are used. Results: For sleep-wake classification, mean Cohen’s kappa between the network estimate and the ground truth annotation by human experts is 0.62. The maximum mean accuracy can reach up to 83% which, to date, is the highest accuracy for sleep-wake classification.

  • Type:

    Article

  • Date:

    01 October 2020

  • Publication Status:

    Published

  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:

    10.1109/access.2020.3028182

  • Cross Ref:

    10.1109/access.2020.3028182

  • Funders:

    Shanghai Municipal Science and Technology Major Project; National Key Research and Development Program of China

Citation

Abbasi, S. F., Ahmad, J., Tahir, A., Awais, M., Chen, C., Irfan, M., …Chen, W. (2020). EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network. IEEE Access, 8, 183025-183034. https://doi.org/10.1109/access.2020.3028182

Authors

Keywords

Neonatal sleep staging, electroencephalogram, classification, multilayer perceptron, neural network

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