f Advancing Sleep Stage Classification with EEG Signal Analysis: LSTM Optimization Using Puffer Fish Algorithm and Explainable AI
FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-120235

Research Article |

Advancing Sleep Stage Classification with EEG Signal Analysis: LSTM Optimization Using Puffer Fish Algorithm and Explainable AI

Author(s): Srinivasa Rao Vemula, Maruthi Vemula, Ghamya Kotapati, Lokesh Sai Kiran Vatsavai, Lakshmi Naga Jayaprada Gavarraju and Ramesh Vatambeti*

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 2

Publisher : FOREX Publication

Published : 25 June 2024

e-ISSN : 2347-470X

Page(s) : 596-604

DOI: https://doi.org/10.37391/IJEER.120235

Abstract

In this study, we introduce SleepXAI, a Convolutional Neural Network-Conditional Random Field (CNN-CRF) technique for automatic multi-class sleep stage classification from polysomnography data. SleepXAI enhances classification accuracy while ensuring explainability by highlighting crucial signal segments. Leveraging Long Short-Term Memory (LSTM) networks, it effectively categorizes epileptic EEG signals. Continuous Wavelet Transform (CWT) optimizes signal quality by analyzing eigenvalue characteristics and removing noise. Eigenvalues, which are scalar values indicating the scaling effect on eigenvectors during linear transformations, are used to ensure clean and representative EEG signals. The Puffer Fish Optimization Algorithm fine-tunes LSTM parameters, achieving heightened accuracy by reducing trainable parameters. Evaluation on the Sleep-EDF-20, Sleep-EDF-78, and SHHS datasets shows promising results, with regular accuracy ranging from 85% to 89%. The proposed LSTM-PFOA algorithm demonstrates efficacy for autonomous sleep categorization network development, promising improved sleep stage classification accuracy and facilitating comprehensive health monitoring practices.

Keywords: Sleep Stage Classification, Convolutional neural network, Continuous Wavelet Transform, Puffer Fish Optimization Algorithm.




Srinivasa Rao Vemula, Software Test Analyst Senior, FIS Management Services, Durham, North Carolina 27703-8589, USA; Email: srinivas.vemula@fisglobal.com

Maruthi Vemula, Student, North Carolina School of Science and Mathematics, Durham, North Carolina 27705, USA; Email: vemula24m@ncssm.edu

Ghamya Kotapati, Department of AI & ML, School of Computing, Mohan Babu University, Tirupati 517102, India; Email: ghamyakotapati@gmail.com

Lokesh Sai Kiran Vatsavai, Department of Information Technology, SRKR Engineering College, Bhimavaram 534204, India; Email: lokesh3069@srkrec.ac.in

Lakshmi Naga Jayaprada Gavarraju, Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, Hyderabad 500100, India; Email: lakshminagajayaprada.g@mrcet.ac.in

Ramesh Vatambeti*, School of Computer Science and Engineering, VIT-AP University, Vijayawada 522237, India; Email: v2ramesh634@gmail.com

    [1] Dutt, M., Redhu, S., Goodwin, M., & Omlin, C. W. (2023). SleepXAI: An explainable deep learning approach for multi-class sleep stage identification. Applied Intelligence, 53(13), 16830-16843.
    [2] Kwon, K., Kwon, S., & Yeo, W. H. (2022). Automatic and accurate sleep stage classification via a convolutional deep neural network and nanomembrane electrodes. Biosensors, 12(3), 155.
    [3] Zhou, D., Wang, J., Hu, G., Zhang, J., Li, F., Yan, R., ... & Cong, F. (2022). SingleChannelNet: A model for automatic sleep stage classification with raw single-channel EEG. Biomedical Signal Processing and Control, 75, 103592.
    [4] Zhao, D., Jiang, R., Feng, M., Yang, J., Wang, Y., Hou, X., & Wang, X. (2022). A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging. Technology and Health Care, 30(2), 323-336.
    [5] Prabhakar, S. K., Rajaguru, H., Ryu, S., Jeong, I. C., & Won, D. O. (2022). A holistic strategy for classification of sleep stages with EEG. Sensors, 22(9), 3557.
    [6] Li, C., Qi, Y., Ding, X., Zhao, J., Sang, T., & Lee, M. (2022). A deep learning method approach for sleep stage classification with EEG spectrogram. International Journal of Environmental Research and Public Health, 19(10), 6322.
    [7] Pei, W., Li, Y., Siuly, S., & Wen, P. (2022). A hybrid deep learning scheme for multi-channel sleep stage classification. Computers, Materials and Continua, 71(1), 889-905.
    [8] Sekkal, R. N., Bereksi-Reguig, F., Ruiz-Fernandez, D., Dib, N., & Sekkal, S. (2022). Automatic sleep stage classification: From classical machine learning methods to deep learning. Biomedical Signal Processing and Control, 77, 103751.
    [9] Goshtasbi, N., Boostani, R., & Sanei, S. (2022). SleepFCN: A fully convolutional deep learning framework for sleep stage classification using single-channel electroencephalograms. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 2088-2096.
    [10] Kim, D., Lee, J., Woo, Y., Jeong, J., Kim, C., & Kim, D. K. (2022). Deep learning application to clinical decision support system in sleep stage classification. Journal of Personalized Medicine, 12(2), 136.
    [11] Satapathy, S. K., & Loganathan, D. (2022). Automated classification of sleep stages using single-channel EEG: A machine learning-based method. International Journal of Information Retrieval Research (IJIRR), 12(2), 1-19.
    [12] ElMoaqet, H., Eid, M., Ryalat, M., & Penzel, T. (2022). A deep transfer learning framework for sleep stage classification with single-channel EEG signals. Sensors, 22(22), 8826.
    [13] Leino, A., Korkalainen, H., Kalevo, L., Nikkonen, S., Kainulainen, S., Ryan, A., ... & Myllymaa, K. (2022). Deep learning enables accurate automatic sleep staging based on ambulatory forehead EEG. IEEE Access, 10, 26554-26566.
    [14] Wang, H., Guo, H., Zhang, K., Gao, L., & Zheng, J. (2022). Automatic sleep staging method of EEG signal based on transfer learning and fusion network. Neurocomputing, 488, 183-193.
    [15] Sri, T. R., Madala, J., Duddukuru, S. L., Reddipalli, R., & Polasi, P. K. (2022, April). A systematic review on deep learning models for sleep stage classification. In 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1505-1511). IEEE.
    [16] You, Y., Zhong, X., Liu, G., & Yang, Z. (2022). Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features. Artificial Intelligence in Medicine, 127, 102279.
    [17] Zhou, X., Ling, B. W. K., Ahmed, W., Zhou, Y., Lin, Y., & Zhang, H. (2024). Multivariate phase space reconstruction and Riemannian manifold for sleep stage classification. Biomedical Signal Processing and Control, 88, 105572.
    [18] Pei, W., Li, Y., Wen, P., Yang, F., & Ji, X. (2024). An automatic method using MFCC features for sleep stage classification. Brain Informatics, 11(1), 1-13.
    [19] Liu, J., Ling, B. W. K., Li, R., Shao, J., Lin, S., Che, J., & Liu, Q. (2024). Sleep stage classification via dynamic mode decomposition approach. Signal, Image and Video Processing, 18(1), 535-544.
    [20] Pan, J., Yu, Y., Wu, J., Zhou, X., He, Y., & Li, Y. (2024). Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients with Disorder of Consciousness. IEEE Transactions on Cognitive and Developmental Systems.
    [21] Zhou, D., Xu, Q., Zhang, J., Wu, L., Xu, H., Kettunen, L., ... & Cong, F. (2024). Interpretable Sleep Stage Classification Based on Layer-wise Relevance Propagation. IEEE Transactions on Instrumentation and Measurement.
    [22] Yeh, P. L., Ozgoren, M., Chen, H. L., Chiang, Y. H., Lee, J. L., Chiang, Y. C., & Chiang, R. P. Y. (2024). Automatic Wake and Deep-Sleep Stage Classification Based on Wigner–Ville Distribution Using a Single Electroencephalogram Signal. Diagnostics, 14(6), 580.
    [23] Heng, X., Wang, M., Wang, Z., Zhang, J., He, L., & Fan, L. (2024). Leveraging discriminative features for automatic sleep stage classification based on raw single-channel EEG. Biomedical Signal Processing and Control, 88, 105631.
    [24] B. Kemp, A. H. Zwinderman, B. Tuk, H. A. C. Kamphuisen, and J. J. L. Oberye, “Analysis of a sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the EEG,” IEEE Trans. Biomed. Eng., vol. 47, no. 9, pp. 1185–1194, Sep. 2000.
    [25] A. L. Goldberger et al., “Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000.
    [26] G.-Q. Zhang et al., “The national sleep research resource: Towards a sleep data commons,” J. Amer. Med. Inform. Assoc., vol. 25, no. 10, pp. 1351–1358, 2018.
    [27] P. Fonseca, N. den Teuling, X. Long, and R. M. Aarts, “Cardiorespiratory sleep stage detection using conditional random fields,” IEEE J. Biomed. Health Informat., vol. 21, no. 4, pp. 956–966, Jul. 2017.
    [28] Khare S.K. and Bajaj V., 2020. Constrained based tunable Q wavelet transform for efficient decomposition of EEG signals. Applied Acoustics, 163, p.107234.
    [29] Thirumalraj, A., & Balasubramanian, P. K. Designing a Modified Grey Wolf Optimizer Based Cyclegan Model for Eeg Mi Classification in Bci.
    [30] Thirumalraj, A., Anusuya, V. S., & Manjunatha, B. (2024). Detection of Ephemeral Sand River Flow Using Hybrid Sandpiper Optimization-Based CNN Model. In Innovations in Machine Learning and IoT for Water Management (pp. 195-214). IGI Global.
    [31] Al-Baik, O., Alomari, S., Alssayed, O., Gochhait, S., Leonova, I., Dutta, U., ... & Dehghani, M. (2024). Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics, 9(2), 65.

Srinivasa Rao Vemula, Maruthi Vemula, Ghamya Kotapati, Lokesh Sai Kiran Vatsavai, Lakshmi Naga Jayaprada Gavarraju and Ramesh Vatambeti (2024), Advancing Sleep Stage Classification with EEG Signal Analysis: LSTM Optimization Using Puffer Fish Algorithm and Explainable AI. IJEER 12(2), 596-604. DOI: 10.37391/IJEER.120235.