Main Article Content



Sleep stages classification using the signal analysis includes electroencephalogram (EEG), Electrooculography (EOG), Electromyography (EMG), Photoplethysmogram (PPG), and electrocardiogram (ECG). In this study, the proposed method using transfer learning to sleep stages classification. First, we have used the PPG and ECG signals, because they are less complex. This signal has the least complexity, and in this article we used this signal for transitional learning. n this study, we extracted 52 features from two signals and prepared for the classification stage. This method includes two steps, (a) Train data PPG and Test data ECG, (b) Train data ECG and Test data PPG. Results proved that our method has acceptable reliability for classification. The accuracy of 95.25% and 94.63% has been reached.

PPG signal, ECG signal, sleep stages classification, transfer learning.

Article Details

How to Cite
MORADI, M. M., FATEHI, M. H., MASOUMI, H., & TAGHIZADEH, M. (2020). TRANSFER LEARNING METHOD FOR SLEEP STAGES CLASSIFICATION USING DIFFERENT DOMAIN. Asian Journal of Advances in Medical Science, 2(3), 21-25. Retrieved from
Original Research Article


Berry RB, et al. AASM - Manual for the Scoring of Sleep and Associated Events version. 2014;2-1.

Wulff K, Gatti S, Wettstein JG, Foster RG. Sleep and circa-dian rhythm disruption in psychiatric and neurodegenerative disease. Nat. Rev. Neurosci. 2010;11(8):589–599.

Ali Abdollahi Gharbali, Shirin Najdi, and José Manuel Fonseca. Transfer learning of spectrogram image for automatic sleep stage classification. Springer, ICIAR 2018, LNCS 10882. 2018;522–528.

Supratak A, Dong H, Wu C, Guo Y. Deep Sleep Net: A model for automatic sleep stage scoring based on Raw Single-Channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 2017;25(11):1998– 2008.

Längkvist M, Karlsson L, Loutfi A. Sleep stage classification using unsupervised feature learning. Adv. Artif. Neural Syst. 2012;1–9.

Supratak A, Dong H, Wu C, Guo Y. Deep Sleep Net: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 2017;25(11):1998–2008.

Vilamala A, Madsen KH, Hansen LK. Deep convolutional neural networks for interpretable analysis of EEG Sleep Stage Scoring; 2017.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;770–778.

Fernando Andreotti1, Huy Phan1, Navin Cooray1, Christine Lo2, Michele TM, Hu2and Maarten De Vos1. Multichannel sleep stage classification and transfer learning using convolutional neural networks. 978-1-5386-3646-6/18/2018 IEEE. 2018;171–174.

Huy Phan†, Oliver Y. Ch ́en, Philipp Koch‡, Alfred Mertins‡, and Maarten De Vos. Deep transfer learning for single-channel automatic sleep staging with channel mismatch. 27th European Signal Processing Conference (EUSIPCO 2019). 2019;1-5.

Hamideh Khadempir, Fatemeh Afsari, Esmat Rashedi. Domain adaptation based on incremental adversarial learning 2020 8th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). Mashhad, Iran; 2020. Available:

Chambon S, Galtier M, Arnal P, Wainrib G, Gramfort A. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series; 2017.

Phan H, Andreotti F, Cooray N, Ch ́en OY, De Vos M. Seq Sleep Net: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging,” IEEE Trans. Neural Systems and Rehabilitation Engineering (TNSRE). 2019;27(3):400–410.

Tsinalis O, Matthews PM, Guo Y, Zafeiriou S. Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks. arXiv:1610.1683. 2016;12.

Stephansen JB. et al. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nature Communications. 2018;9(1):5229.

Weber F, Dan Y. Circuit-based interrogation of sleep control. Nature. 2016;538(7623):51–59.