Vol.8, No.1, February 2019.                                                                                                                                                                             ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

ROM-based Inference Method Built on Deep Learning for Sleep Stage Classification


Mohamed H. AlMeer, Hanadi Hassen, Naveed Nawaz


© 2019 Mohamed H. AlMeer, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 8, Issue 1, Pages 28-40, ISSN 2217-8309, DOI: 10.18421/TEM81-04, February 2019.


Received: 02 February 2019.
Accepted: 23 February 2019.
Published: 27 February 2019.




We used a classical deep feedforward neural network (DFFNN) for an automatic sleep stage scoring based on a single-channel EEG signal. We used an open-available dataset, randomly selecting one healthy young adult for both training (≈5%) and evaluation (≈95%). We also augmented the validation by using 5-fold cross validations for the result comparisons. We introduced a new method for inferring the trained network based on a ROM module (memory concept), so it would be faster than directly inferring the trained Deep Neural Network (DNN). The ROM content is filled after the DNN network is trained by the training set and inferred using the testing set. An accuracy of 97% was achieved in inferring the test datasets using ROM when compared to the classic trained DNN inference process.


Keywords – PSG, Sleep stages, Deep Neural Networks, DNN, FFNN.



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