Skip to main content
Log in

A new automatic sleep stage classification model using swarm intelligence-based hybrid transfer learning architecture

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

  1. 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)

    PubMed  Google Scholar 

  2. 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)

    Article  PubMed  Google Scholar 

  3. Qu, W.: A residual based attention model for EEG based sleep staging. IEEE J. Biomed. Health Inform. 24(10), 2833–2843 (2020)

    Article  PubMed  Google Scholar 

  4. 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)

    Article  PubMed  Google Scholar 

  5. 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)

    Article  PubMed  Google Scholar 

  6. Leino, A.: Deep learning enables accurate automatic sleep staging based on ambulatory forehead EEG. IEEE Access 10, 26554–26566 (2022)

    Article  Google Scholar 

  7. 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)

    Article  PubMed  Google Scholar 

  8. 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)

    Article  PubMed  Google Scholar 

  9. 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)

    Article  PubMed  Google Scholar 

  10. Jadhav, P., Mukhopadhyay, S.: Automated sleep stage scoring using time-frequency spectra convolution neural network. IEEE Trans. Instrum. Meas. 71, 1–9 (2022)

    Article  Google Scholar 

  11. Zhou, D.: Alleviating class imbalance problem in automatic sleep stage classification. IEEE Trans. Instrum. Meas. 71, 1–12 (2022)

    CAS  Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. 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)

    Article  CAS  PubMed  Google Scholar 

  15. Baek, J.: Automatic sleep scoring using intrinsic mode based on interpretable deep neural networks. IEEE Access 10, 36895–36906 (2022)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Article  PubMed  PubMed Central  Google Scholar 

  18. Guillot, E.A., Thorey, V.: RobustSleepNet: transfer learning for automated sleep staging at scale. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1441–1451 (2021)

    Article  PubMed  Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. 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)

    Article  PubMed  Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. 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)

    Article  PubMed  Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. 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)

  28. 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)

    Article  Google Scholar 

  29. Dehghani, M., Hubálovský, Š, Trojovský, P.: Cat and mouse based optimizer: a new nature-inspired optimization algorithm. Sensors 21(15), 5214 (2021)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  30. 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)

    Article  ADS  PubMed  Google Scholar 

  31. 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)

    Google Scholar 

  32. 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)

  33. 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)

    Article  CAS  PubMed  Google Scholar 

  34. 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)

  35. 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)

    Article  ADS  Google Scholar 

  36. 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)

    Article  Google Scholar 

  37. Yu, W., Lv, P.: An end-to-end intelligent fault diagnosis application for rolling bearing based on MobileNet. IEEE Access 9, 41925–41933 (2021)

    Article  Google Scholar 

  38. 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)

    Article  Google Scholar 

  39. 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)

    Article  ADS  Google Scholar 

  40. Zhang, K., Guo, Y., Wang, X., Yuan, J., Ding, Q.: Multiple feature reweight densenet for image classification. IEEE Access 7, 9872–9880 (2019)

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific funding.

Author information

Authors and Affiliations

Authors

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

Correspondence to A. Ravi Raja.

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.

Supplementary file 1 (DOCX 1830 kb)

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02792-9

Keywords

Navigation