Abstract
Existing automatic sleep stage classification systems have mostly relied on hand-crafted features selected from polysomnographic records. To measure the quality of sleep, the automatic sleep stage classification system is very important. The sleep specialists examine the signals such as Electromyograms, Electroencephalograms (EEG), Electrocardiograms, and Electrooculograms, based on the visual inspection that is assigned every 30 s of the signal at the sleep stage. Hence, this research plans to implement an effective sleep stage classification model for detecting sleep disorder patients. It is performed with filtering approaches together with artifact removal techniques to get the pre-processed EEG signals. This pre-processed signal is used in the signal decomposition phase, where the short-time Fourier transform is involved in decomposing the pre-processed signals. Furthermore, these decomposed EEG signals are utilized in the optimal hybrid transfer learning approach for sleep stage classification using Mobilenet and Densenet techniques. The optimization takes place in the hybrid transfer learning approach with the development of a hybrid optimization strategy Hybrid Coyote Cat and Mouse Optimization Algorithm, to make efficient and accurate classification results. Experimental analysis reveals that the developed approach attains better effectiveness by analyzing various comparative techniques using different performance measures.
Similar content being viewed by others
Data availability
The data underlying this article are available in the database of dataset 1 from the Sleep-EDF Database Expanded dataset, at https://physionet.org/content/sleep-edfx/1.0.0/, and dataset 2 from St. Vincent's University Hospital/University College Dublin Sleep Apnea dataset, at https://physionet.org/content/sleep-edfx/1.0.0/.
References
Korkalainen, H.: Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea. IEEE J. Biomed. Health Inform. 24(7), 2073–2081 (2020)
Goshtasbi, N., Boostani, R., Sanei, S.: SleepFCN: a fully convolutional deep learning framework for sleep stage classification using single-channel electroencephalograms. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2088–2096 (2022)
Qu, W.: A residual based attention model for EEG based sleep staging. IEEE J. Biomed. Health Inform. 24(10), 2833–2843 (2020)
Banluesombatkul, N.: MetaSleepLearner: a pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning. IEEE J. Biomed. Health Inform. 25(6), 1949–1963 (2021)
Chambon, S., Galtier, M.N., Arnal, P.J., Wainrib, G., Gramfort, A.: A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans. Neural Syst. Rehabil. Eng. 26(4), 758–769 (2018)
Leino, A.: Deep learning enables accurate automatic sleep staging based on ambulatory forehead EEG. IEEE Access 10, 26554–26566 (2022)
Eldele, E.: An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 809–818 (2021)
Guillot, A., Sauvet, F., During, E.H., Thorey, V.: Dreem open datasets: multi-scored sleep datasets to compare human and automated sleep staging. IEEE Trans. Neural Syst. Rehabil. Eng. 28(9), 1955–1965 (2020)
Kwon, H.B., et al.: Attention-based LSTM for non-contact sleep stage classification using IR-UWB radar. IEEE J. Biomed. Health Inform. 25(10), 3844–3853 (2021)
Jadhav, P., Mukhopadhyay, S.: Automated sleep stage scoring using time-frequency spectra convolution neural network. IEEE Trans. Instrum. Meas. 71, 1–9 (2022)
Zhou, D.: Alleviating class imbalance problem in automatic sleep stage classification. IEEE Trans. Instrum. Meas. 71, 1–12 (2022)
Cai, Q., Gao, Z., An, J., Gao, S., Grebogi, C.: A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from EEG signals. IEEE Trans. Circuits Syst. II Express Briefs 68(2), 777–781 (2021)
Jia, Z., Cai, X., Zheng, G., Wang, J., Lin, Y.: SleepPrintNet: a multivariate multimodal neural network based on physiological time-series for automatic sleep staging. IEEE Trans. Artif. Intell. 1(3), 248–257 (2020)
Willemen, T.: An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification. IEEE J. Biomed. Health Inform. 18(2), 661–669 (2014)
Baek, J.: Automatic sleep scoring using intrinsic mode based on interpretable deep neural networks. IEEE Access 10, 36895–36906 (2022)
Sekkal, R.N., Bereksi-Reguig, F., Ruiz-Fernandez, D., Dib, N., Sekkal, S.: Automatic sleep stage classification: from classical machine learning methods to deep learning. Biomed. Signal Process. Control 77, 103751 (2022)
Kwon, K., Kwon, S., Yeo, W.-H.: Automatic and accurate sleep stage classification via a convolutional deep neural network and nanomembrane electrodes. Biosensors 12(3), 155 (2022)
Guillot, E.A., Thorey, V.: RobustSleepNet: transfer learning for automated sleep staging at scale. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1441–1451 (2021)
Abdollahpour, M., Rezaii, T.Y., Farzamnia, A., Saad, I.: Transfer learning convolutional neural network for sleep stage classification using two-stage data fusion framework. IEEE Access 8, 180618–180632 (2020)
Chambon, S., Galtier, M.N., Arnal, P.J., Wainrib, G., Gramfort, A.: A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans. Neural Syst. Rehabil. Eng. 26(4), 758–769 (2018)
Liao, Y., Zhang, C., Zhang, M., Wang, Z., Xie, X.: LightSleepNet: design of a personalized portable sleep staging system based on single-channel EEG. IEEE Trans. Circuits Syst. II Express Briefs 69(1), 224–228 (2022)
He, Z., Tang, M., Wang, P., Du, L., Chen, X., Cheng, G., Fang, Z.: Cross-scenario automatic sleep stage classification using transfer learning and single-channel EEG. Biomed. Signal Process. Control 81, 104501 (2023)
Efe, E., Ozsen, S.: CoSleepNet: automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets. Biomed. Signal Process. Control 80, 104299 (2023)
Zhou, D., Wang, J., Hu, G., Zhang, J., Li, F., Yan, R., Kettunen, L., Chang, Z., Xu, Q., Cong, F.: SingleChannelNet: a model for automatic sleep stage classification with raw single-channel EEG. Biomed. Signal Process. Control 75, 103592 (2022)
Zhang, J., Yao, R., Ge, W., Gao, J.: Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Comput. Methods Programs Biomed. 183, 105089 (2020)
Patil, N.S., Patil, S.M., Raut, C.M., Pande, A.P., Yeruva, A.R., Morwani, H.: An efficient approach for object detection using deep learning. J. Pharm. Negat. Results 13(SI-9), 563–572 (2022)
Rana, A., Reddy, A., Shrivastava, A., Verma, D., Ansari, M. S., Singh, D.: Secure and smart healthcare system using IoT and deep learning models. In: 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), pp. 915–922 (2022)
Yuan, Z., Wang, W., Wang, H., Yildizbasi, A.: Developed coyote optimization algorithm and its application to optimal parameters estimation of PEMFC model. Energy Rep. 6, 1106–1117 (2020)
Dehghani, M., Hubálovský, Š, Trojovský, P.: Cat and mouse based optimizer: a new nature-inspired optimization algorithm. Sensors 21(15), 5214 (2021)
Zhang, W., Yang, W., Jiang, X., Qin, X., Yang, J., Du, J.: Two-stage intelligent multi-type artifact removal for single-channel EEG settings: a GRU autoencoder based approach. IEEE Trans. Biomed. Eng. 69(10), 3142–3154 (2022)
Pei, S.-C., Huang, S.-G.: 2-D laguerre distributed approximating functional: a circular low-pass/band-pass filter. IEEE Trans. Circuits Syst. II Express Briefs 66(5), 818–822 (2019)
Zhou, D., Xu, Q., Wang, J., Zhang, J., Hu, G., Kettunen, L., Chang, Z., Cong, F.: LightSleepNet: a lightweight deep model for rapid sleep stage classification with spectrograms. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 43–46. IEEE (2021)
Li, Y., Peng, C., Zhang, Y., Zhang, Y., Lo, B.: Adversarial learning for semi-supervised pediatric sleep staging with single-EEG channel. Methods 204, 84–91 (2022)
Xu, Q., Zhou, D., Wang, J., Shen, J., Kettunen, L., Cong, F.: Convolutional neural network based sleep stage classification with class imbalance. In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE (2022)
Zhu, W., Li, X., Liu, C., Xue, F., Han, Y.: An STFT-LSTM system for P-wave identification. IEEE Geosci. Remote Sens. Lett. 17(3), 519–523 (2020)
Huang, Z., Zhu, X., Ding, M., Zhang, X.: Medical image classification using a light-weighted hybrid neural network based on PCANet and DenseNet. IEEE Access 8, 24697–24712 (2020)
Yu, W., Lv, P.: An end-to-end intelligent fault diagnosis application for rolling bearing based on MobileNet. IEEE Access 9, 41925–41933 (2021)
Kanna, S.K.R., Sivakumar, K., Lingaraj, N.: Development of deer hunting linked earthworm optimization algorithm for solving large scale traveling salesman problem. Knowl. Based Syst. 227, 1071995 (2021)
Seo, J.-H., Im, C.-H., Kwak, S.-Y., Lee, C.-G., Jung, H.-K.: An improved particle swarm optimization algorithm mimicking territorial dispute between groups for multimodal function optimization problems. IEEE Trans. Magn. 44(6), 1046–1049 (2008)
Zhang, K., Guo, Y., Wang, X., Yuan, J., Ding, Q.: Multiple feature reweight densenet for image classification. IEEE Access 7, 9872–9880 (2019)
Funding
This research did not receive any specific funding.
Author information
Authors and Affiliations
Contributions
All authors have made substantial contributions to the conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Ethical approval
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Raja, A.R., Polasi, P.K. A new automatic sleep stage classification model using swarm intelligence-based hybrid transfer learning architecture. SIViP 18, 1131–1142 (2024). https://doi.org/10.1007/s11760-023-02792-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-023-02792-9