Abstract
This study presents a method for designing--by a genetic algorithm, without manual intervention--the feature learning architecture for classification of sleep stages from a single EEG channel, when using a convolutional neural network called GACNN SleepTuneNet. Two EEG electrode positions were selected, namely FP2-F4 and FPz-Cz, from two available datasets. Twenty-five generations were involved in diagnosis without hand-crafted features, to learn the architecture for classification of sleep stages based on AASM standard. Based on the results, our model not only achieved the highest classification accuracy, but it also distinguished the sleep stages based on either of the two EEG electrode signals, in both datasets. The results show that our model performed the best with highest overall accuracy rates and kappa statistic (CAP sleep: 95.61 % and 0.94; Sleep EDF: 92.51 % and 0.90) among other state-of-the-art methods that require no manual intervention. Our model could automatically learn the features for classification of sleep stages, for different raw EEG electrode positions in different datasets, without user-assisted feature extraction.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.