Addressing Imbalanced EEG Data for Improved Microsleep Detection: An ADASYN, FFT and LDA-Based Approach

Md Mahmudul Hasan; Sayma Khandaker; Norizam Sulaiman; Mirza Mahfuj Hossain; Ashraful Islam

doi:10.24237/djes.2024.17304

Authors

Md Mahmudul Hasan Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia
Sayma Khandaker Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia
Norizam Sulaiman
norizam@umpsa.edu.my
Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia
Mirza Mahfuj Hossain Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore 7408, Bangladesh
Ashraful Islam Department of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore 7408, Bangladesh

Keywords:

Microsleep detection, EEG signal, ADASYN, FFT, LDA

Abstract

Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preventing accidents. This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. After preprocessing, features are extracted using Fast Fourier Transform (FFT), which provides a comprehensive frequency domain analysis of the EEG signals. For classification, Linear Discriminant Analysis (LDA) is employed to effectively distinguish between microsleep events and normal wakefulness based on the extracted features. The proposed framework was rigorously validated using a well-established publicly available EEG dataset, which included recordings from 76 healthy individuals. The validation results revealed a high testing accuracy of 92.71% in detecting microsleep episodes, demonstrating the effectiveness of the proposed approach. These results underscore the potential of combining EEG signal analysis with machine learning models for practical applications in monitoring driver alertness. The framework could significantly enhance driver safety by providing an effective tool for detecting microsleep and thereby reducing the risk of accidents caused by drowsy driving. This research highlights the promising application of advanced signal processing and machine learning techniques in the field of driver alertness monitoring.

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References

M. T. R. Peiris, R. D. Jones, P. R. Davidson, G. J. Carroll, and P. J. Bones, “Frequent lapses of responsiveness during an extended visuomotor tracking task in non‐sleep‐deprived subjects,” Journal of Sleep Research, vol. 15, no. 3, pp. 291–300, Sep. 2006.

P. R. Davidson, R. D. Jones, and M. T. R. Peiris, “EEG-Based Lapse Detection with High Temporal Resolution,” IEEE Trans. Biomed. Eng., vol. 54, no. 5, pp. 832–839, May 2007.

W. Vanlaar, H. Simpson, D. Mayhew, and R. Robertson, “Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors,” Journal of Safety Research, vol. 39, no. 3, pp. 303–309, Jan. 2008.

G. R. Poudel, C. R. H. Innes, P. J. Bones, R. Watts, and R. D. Jones, “Losing the struggle to stay awake: Divergent thalamic and cortical activity during microsleeps: Neural Activity During Microsleeps,” Hum. Brain Mapp, vol. 35, no. 1, pp. 257–269, Jan. 2014.

M. M. Hasan, M. H. Mirza, and N. Sulaiman, “Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal,” Applications of Modelling and Simulation, vol. 7, pp. 178–1892023, 2023.

R. Magjarevic, B. Sirois, U. Trutschel, D. Edwards, D. Sommer, and M. Golz, “Predicting Accident Probability from Frequency of Microsleep Events,” in World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, vol. 25/4, O. Dössel and W. C. Schlegel, Eds., in IFMBE Proceedings, vol. 25/4, Berlin, Heidelberg: Springer Berlin Heidelberg, , pp. 2284–2286, 2009.

S. K. B. Sangeetha, S. K. Mathivanan, V. Muthukumaran, N. Pughazendi, P. Jayagopal, and M. S. Uddin, “A Deep Learning Approach to Detect Microsleep Using Various Forms of EEG Signal,” Mathematical Problems in Engineering, vol. 2023, no. 1, p. 7317938, Jan. 2023.

R. Sharma, R. B. Pachori, and A. Upadhyay, “Automatic sleep stages classification based on iterative filtering of electroencephalogram signals,” Neural Comput & Applic, vol. 28, no. 10, pp. 2959–2978, Oct. 2017.

Y. Chu, X. Zhao, J. Han, Y. Zhao, and J. Yao, “SSVEP based brain-computer interface controlled functional electrical stimulation system for upper extremity rehabilitation,” in 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), Bali, Indonesia: IEEE, pp. 2244–2249, Dec. 2014.

M. M. Hasan, M. M. Hossain, N. Sulaiman, and S. Khandaker, “Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal,” JTEC, vol. 16, no. 1, pp. 25–31, Mar.

S. K. B. Sangeetha, R. Dhaya, D. T. Shah, R. Dharanidharan, and K. P. S. Reddy, “An empirical analysis of machine learning frameworks for digital pathology in medical science,” J. Phys.: Conf. Ser., vol. 1767, no. 1, p. 012031, Feb. 2021

G. C. Gutiérrez-Tobal, D. Álvarez, J. V. Marcos, F. Del Campo, and R. Hornero, “Pattern recognition in airflow recordings to assist in the sleep apnoea–hypopnoea syndrome diagnosis,” Med Biol Eng Comput, vol. 51, no. 12, pp. 1367–1380, Dec. 2013

S. K. B. Sangeetha and R. Dhaya, “Deep Learning Era for Future 6G Wireless Communications—Theory, Applications, and Challenges,” in Artificial Intelligent Techniques for Wireless Communication and Networking, 1st ed., R. Kanthavel, K. Ananthajothi, S. Balamurugan, and R. K. Ganesh, Eds., Wiley, pp. 105–119, 2022

M.-Y. Chen, H.-S. Chiang, A. K. Sangaiah, and T.-C. Hsieh, “Recurrent neural network with attention mechanism for language model,” Neural Comput & Applic, vol. 32, no. 12, pp. 7915–7923, Jun. 2020.

D. Kanthavel, S. K. B. Sangeetha, and K. P. Keerthana, “An empirical study of vehicle to infrastructure communications - An intense learning of smart infrastructure for safety and mobility,” International Journal of Intelligent Networks, vol. 2, pp. 77–82, 2021.

R. Jabbar, M. Shinoy, M. Kharbeche, K. Al-Khalifa, M. Krichen, and K. Barkaoui, “Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application,” in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar: IEEE, pp. 237–242, Feb. 2020.

R. Malekian, A. F. Kavishe, B. T. Maharaj, P. K. Gupta, G. Singh, and H. Waschefort, “Smart Vehicle Navigation System Using Hidden Markov Model and RFID Technology,” Wireless Pers Commun, vol. 90, no. 4, pp. 1717–1742, Oct. 2016.

D. Mollicone et al., “Predicting performance and safety based on driver fatigue,” Accident Analysis & Prevention, vol. 126, pp. 142–145, May 2019.

D. Singh, B. Pati, C. R. Panigrahi, and S. Swagatika, “Security Issues in IoT and their Countermeasures in Smart City Applications,” in Advanced Computing and Intelligent Engineering, vol. 1089, B. Pati, C. R. Panigrahi, R. Buyya, and K.-C. Li, Eds., in Advances in Intelligent Systems and Computing, vol. 1089, Singapore: Springer Singapore, pp. 301–313, 2020

M. Miranda, A. Villanueva, M. J. Buo, R. Merabite, S. P. Perez, and J. M. Rodriguez, “Portable Prevention and Monitoring of Driver’s Drowsiness Focuses to Eyelid Movement Using Internet of Things,” in 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines: IEEE, pp. 1–5, Nov. 2018.

M. T. R. Peiris, R. D. Jones, P. R. Davidson, P. J. Bones, and D. J. Myall, “Fractal Dimension of the EEG for Detection of Behavioural Microsleeps,” in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China: IEEE, pp. 5742–5745, 2005.

P. R. Davidson, R. D. Jones, and M. T. R. Peiris, “Detecting Behavioral Microsleeps using EEG and LSTM Recurrent Neural Networks,” in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China: IEEE, pp. 5754–5757, 2005.

M. T. R. Peiris, P. R. Davidson, P. J. Bones, and R. D. Jones, “Detection of lapses in responsiveness from the EEG,” J. Neural Eng., vol. 8, no. 1, p. 016003, Feb. 2011.

M. Golz, D. Sommer, and J. Krajewski, “Prediction of immediately occurring microsleep events from brain electric signals,” Current Directions in Biomedical Engineering, vol. 2, no. 1, pp. 149–153, Sep. 2016.

A. Picot, S. Charbonnier, and A. Caplier, “On-line automatic detection of driver drowsiness using a single electroencephalographic channel,” in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC: IEEE, pp. 3864–3867, Aug. 2008.

Y.-S. Kweon, H.-G. Kwak, G.-H. Shin, and M. Lee, “Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG,” presented at the 2021 9th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South): IEEE, , pp. 1–6, 2021.

R. Shoorangiz, S. J. Weddell, and R. D. Jones, “Prediction of microsleeps from EEG: Preliminary results,” presented at the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA: IEEE, pp. 4650–4653, 2016.

H.-G. Anneke, S. Jelena, M. Alexander, A. Peter, M. Johannes, and S. D. R, “Maintenance of Wakefulness Test (MWT) recordings.” Zenodo, Sep. 27, 2019.

A. Luque, A. Carrasco, A. Martín, and A. De Las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognition, vol. 91, pp. 216–231, Jul. 2019.

K. Alkharabsheh, S. Alawadi, V. R. Kebande, Y. Crespo, M. Fernández-Delgado, and J. A. Taboada, “A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class,” Information and Software Technology, vol. 143, p. 106736, Mar. 2022.

Haibo He, Yang Bai, E. A. Garcia, and Shutao Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China: IEEE, , pp. 1322–1328, Jun. 2008.

Y. Li, W. Xu, W. Li, A. Li, Z. Liu, and School of Computer Science and Technology, Donghua University, Shanghai 201620, China, “Research on hybrid intrusion detection method based on the ADASYN and ID3 algorithms,” MBE, vol. 19, no. 2, pp. 2030–2042, 2021.

T. M. Khan, S. Xu, Z. G. Khan, and M. U. chishti, “Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System,” Journal of Healthcare Engineering, vol. 2021, no. 1, Mar. 2021, doi: https://doi.org/10.1155/2021/5577636.

C.-S. Huang, C.-L. Lin, L.-W. Ko, S.-Y. Liu, T.-P. Su, and C.-T. Lin, “Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels,” Front. Neurosci., vol. 8, Sep. 2014.

K. Samiee, P. Kovacs, and M. Gabbouj, “Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform,” IEEE Trans. Biomed. Eng., vol. 62, no. 2, pp. 541–552, Feb. 2015.

R. Atangana, D. Tchiotsop, G. Kenne, and L. C. DjoufackNkengfac K, “EEG Signal Classification using LDA and MLP Classifier,” HIIJ, vol. 9, no. 1, pp. 14–32, Feb. 2020.

A. Subasi and M. Ismail Gursoy, “EEG signal classification using PCA, ICA, LDA and support vector machines,” Expert Systems with Applications, vol. 37, no. 12, pp. 8659–8666, Dec. 2010.

A. Chaudhuri and A. Routray, “Driver Fatigue Detection Through Chaotic Entropy Analysis of Cortical Sources Obtained from Scalp EEG Signals,” IEEE Trans. Intell. Transport. Syst., vol. 21, no. 1, pp. 185–198, Jan. 2020.

C. Zhao, C. Zheng, M. Zhao, Y. Tu, and J. Liu, “Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic,” Expert Systems with Applications, vol. 38, no. 3, pp. 1859–1865, Mar. 2011.

T. Nguyen, S. Ahn, H. Jang, S. C. Jun, and J. G. Kim, “Utilization of a combined EEG/NIRS system to predict driver drowsiness,” Sci Rep, vol. 7, no. 1, p. 43933, Mar. 2017.

R. Chai et al., “Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System,” IEEE J. Biomed. Health Inform., vol. 21, no. 3, pp. 715–724, May 2017.

M. Ogino and Y. Mitsukura, “Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram,” Sensors, vol. 18, no. 12, p. 4477, Dec. 2018.

R. Chai et al., “Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System,” IEEE J. Biomed. Health Inform., vol. 21, no. 3, pp. 715–724, May 2017.

S. Ahn, T. Nguyen, H. Jang, J. G. Kim, and S. C. Jun, “Exploring Neuro-Physiological Correlates of Drivers’ Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data,” Front. Hum. Neurosci., vol. 10, May 2016.

A. Garcés Correa, L. Orosco, and E. Laciar, “Automatic detection of drowsiness in EEG records based on multimodal analysis,” Medical Engineering & Physics, vol. 36, no. 2, pp. 244–249, Feb. 2014.