Sleep Stage Classification using NeuCube on SpiNNaker: a Preliminary Study

Sugam Budhraja, Basabdatta Bhattacharya, Simon Durrant, Zohreh Doborjeh, Maryam Doborjeh, Nikola Kasabov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper studies sleep stage classification using NeuCube, a Spiking Neural Network (SNN) architecture, simulated on SpiNNaker, a neuromorphic computer. The sleep electroencephalogram (EEG) time series is converted to spikes
and provided as an input to NeuCube. Relevant feature vectors are extracted at different stages of training. We used six standard machine learning classifiers on different combinations of these feature vectors and calculated 5-fold cross-validation accuracy. We observed that the gradient boosted decision trees classifier performed the best by achieving 81.25% accuracy on a combination
of two feature vectors. An evaluation of the results using confusion matrices and classification reports showed that the Awake, N2, SWS and REM sleep stages can be classified with 80% F1-score using the gradient boosted decision trees algorithm. Overall, our proof-of-concept work towards autonomous sleep stage
classification using NeuCube shows promise and will form the base for continued research in this direction.
Original languageEnglish
Title of host publicationProceedings of the IEEE WCCI 2020
PublisherIEEE Computational Intelligence Society
Number of pages8
VolumeWCCI2020 - IJCNN2020 Proceedings
Publication statusAccepted/In press - 21 Mar 2020


  • Sleep stage classification
  • EEG
  • spiking neural networks
  • SpiNNaker


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