Predicting Sleep Disorder using Raw Multi-Channel EEG signal

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dc.contributor.authorJarif, Afnan
dc.contributor.authorRahman, Asfi
dc.contributor.authorPrima, Tasfia Tabassum
dc.date.accessioned2024-01-18T08:32:32Z
dc.date.available2024-01-18T08:32:32Z
dc.date.issued2023-05-30
dc.descriptionSupervised Ms. Lutfun Nahar Lota, Assistant Professor, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladeshen_US
dc.description.abstractAccurate sleep stage scoring and finding relevant feature form multi-channel EEG signal form different subject (healthy and unhealthy) is complex task. In recent years, deep learning, a type of machine learning that involves training ar tificial neural networks on large data sets, has shown promise for improving the accuracy and reliability of sleep stage scoring. This approach involves analyzing the pre-processed raw data and extract important feature and try to find informa tion and based on that we predict if a person has sleep disorder or not. By using deep learning to train our model the extracted data set we reprocessed and find promising result, it is possible to develop more accurate algorithms or models for automatic prediction of sleep disorder and other abnormal activity in brainen_US
dc.identifier.citation1. Supratak, A., Dong, H., Wu, C. & Guo, Y. DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, 1998–2008 (2017). 2. Lajnef, T. et al. Learning machines and sleeping brains: automatic sleep stage clas sification using decision-tree multi-class support vector machines. Journal of neuro science methods 250, 94–105 (2015). 3. Huang, C.-S. et al. Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels. Frontiers in neuroscience 8, 263 (2014). 27 4. Tsinalis, O., Matthews, P. M. & Guo, Y. Automatic sleep stage scoring using time frequency analysis and stacked sparse autoencoders. Annals of biomedical engineer ing 44, 1587–1597 (2016). 5. Hassan, A. R. & Subasi, A. A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowledge-Based Systems 128, 115–124 (2017). 6. L¨angkvist, M., Karlsson, L. & Loutfi, A. Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems 2012 (2012). 7. Mousavi, S., Afghah, F. & Acharya, U. R. SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PloS one 14, e0216456 (2019). 8. Schuster, M. & Paliwal, K. Networks bidirectional reccurent neural. IEEE Trans Signal Proces 45, 2673–2681 (1997). 9. Yang, S. & Deravi, F. Wavelet-based EEG preprocessing for biometric applications in 2013 fourth international conference on emerging security technologies (2013), 43–46. 10. Al-Fahoum, A. S. & Al-Fraihat, A. A. Methods of EEG signal features extraction us ing linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014). 11. Lee, H. & Choi, S. Pca+ hmm+ svm for eeg pattern classification in Seventh Inter national Symposium on Signal Processing and Its Applications, 2003. Proceedings. 1 (2003), 541–544. 12. Roy, S., Kiral-Kornek, I. & Harrer, S. ChronoNet: a deep recurrent neural network for abnormal EEG identification in Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings 17 (2019), 47–56. 13. Zarei, A., Beheshti, H. & Asl, B. M. Detection of sleep apnea using deep neural networks and single-lead ECG signals. Biomedical Signal Processing and Control 71, 103125 (2022). 28 14. Zhao, W. et al. EEG spectral analysis in insomnia disorder: A systematic review and meta-analysis. Sleep Medicine Reviews 59, 101457 (2021). 15. Liu, D., Pang, Z. & Lloyd, S. R. A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG. IEEE Transactions on Neural Networks 19, 308–318 (2008).en_US
dc.identifier.urihttp://hdl.handle.net/123456789/2065
dc.language.isoenen_US
dc.publisherDepartment of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladeshen_US
dc.titlePredicting Sleep Disorder using Raw Multi-Channel EEG signalen_US
dc.typeThesisen_US

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