@article{MAKHILLJEAS2019141618205,
    title = {ROM-based Inference Method Built on Deep Learning for Sleep Stage Classification},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {16},
    pages = {5906-5916},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.5906.5916},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.5906.5916},
    author = {Mohamed H.,Hanadi and},
    keywords = {deep neural networks,PSG,sleep stages,DNN,FFNN,ROM content},
    abstract = {We used a classical Deep Feed Forward 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.}
    }