Sleep Stages Classification Using Music Made From EEG
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Abstract
Purpose: Automatic classification of sleep stages is one of the fundamental factors in diagnosing sleep disorders to prevent and treat various diseases, and it can significantly aid in saving specialists' time and energy. In this study, a novel method for mapping electroencephalogram (EEG) signals to music for sleep stage classification is proposed.
Materials and Methods: A total of 15.233, 30-second data segments from the Sleep-EDF database were used as the statistical population for this evaluation. Initially, single-channel EEG data are mapped to musical pieces using a long short-term memory (LSTM) network structure. Subsequently, seven features are extracted from the generated music sequences and applied to classification structures.
Results: The overall classification accuracy for the five sleep stages according to the AASM standard is 85.3% for the Sleep-EDF database. Another objective of this study is to present a novel single-channel EEG sonification method, achieving classification accuracy that is either higher than or comparable to contemporary methods.
Conclusion: The results of this study show that this audio signal mapping contains effective information for sleep stage classification and the proposed method performs well compared to new methods without the need for complex classifier structures.
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17- Sam V Norman-Haignere, Philippe Albouy, Anne Caclin, Josh H McDermott, Nancy G Kanwisher, and Barbara Tillmann, "Pitch-responsive cortical regions in congenital amusia." Journal of Neuroscience, Vol. 36 (No. 10), pp. 2986-94, (2016).
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21- Clara E James, Mathias S Oechslin, Christoph M Michel, and Michael De Pretto, "Electrical neuroimaging of music processing reveals mid-latency changes with level of musical expertise." Frontiers in neuroscience, Vol. 11p. 613, (2017).
22- Dan Wu, Chaoyi Li, and Dezhong Yao, "Scale-free brain quartet: artistic filtering of multi-channel brainwave music." PLoS One, Vol. 8 (No. 5), p. e64046, (2013).
23- Jing Lu et al., "Generate the scale-free brain music from BOLD signals." Medicine, Vol. 97 (No. 2), (2018).
24- Carlos M Fernandes, Daria Migotina, and Agostinho C Rosa, "Brain's Night Symphony (BraiNSy): A Methodology for EEG Sonification." IEEE Transactions on Affective Computing, Vol. 12 (No. 1), pp. 103-12, (2018).
25- Carlos M Fernandes, Antonio Mora, Juan Julián Merelo, and Agostinho C Rosa, "Pherogenic Drawings-Generating Colored 2-dimensional Abstract Representations of Sleep EEG with the KANTS Algorithm." in International Conference on Evolutionary Computation Theory and Applications, (2012), Vol. 2: SCITEPRESS, pp. 72-80.
26- Pedro Franco and Alpo Värri, "Experiments of the sonification of the sleep electroencephalogram." Finnish Journal of eHealth and eWelfare, Vol. 7 (No. 2-3), pp. 65-74, (2015).
27- Aurora Tulilaulu, Joonas Paalasmaa, Mikko Waris, and Hannu Toivonen, "Sleep musicalization: Automatic music composition from sleep measurements." in Advances in Intelligent Data Analysis XI: 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings 11, (2012): Springer, pp. 392-403.
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29- Bob Kemp, Aeilko Zwinderman, B Tuk, H Kamphuisen, and J Oberyé, "Sleep-edf database expanded." Physionet org, (2018).
30- Boualem Boashash, "Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals." Proceedings of the IEEE, Vol. 80 (No. 4), pp. 520-38, (1992).
31- Sotiris B Kotsiantis, "Bagging and boosting variants for handling classifications problems: a survey." The Knowledge Engineering Review, Vol. 29 (No. 1), pp. 78-100, (2014).
32- Sanidhya Mangal, Rahul Modak, and Poorva Joshi, "LSTM based music generation system." arXiv preprint arXiv:1908.01080, (2019).
33- Sarthak Agarwal, Vaibhav Saxena, Vaibhav Singal, and Swati Aggarwal, "Lstm based music generation with dataset preprocessing and reconstruction techniques." in 2018 IEEE symposium series on computational intelligence (SSCI), (2018): IEEE, pp. 455-62.
34- Sepp Hochreiter and Jürgen Schmidhuber, "Long short-term memory." Neural computation, Vol. 9 (No. 8), pp. 1735-80, (1997).
35- João Paulo Teixeira, Carla Oliveira, and Carla Lopes, "Vocal acoustic analysis–jitter, shimmer and hnr parameters." Procedia Technology, Vol. 9pp. 1112-22, (2013).
36- Yoav Goldberg, Neural network methods for natural language processing. Springer Nature, (2022).
37- Albert Vilamala, Kristoffer H Madsen, and Lars K Hansen, "Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring." in 2017 IEEE 27th international workshop on machine learning for signal processing (MLSP), (2017): IEEE, pp. 1-6.
38- Nicola Michielli, U Rajendra Acharya, and Filippo Molinari, "Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals." Computers in biology and medicine, Vol. 106pp. 71-81, (2019).
39- Ahnaf Rashik Hassan and Mohammed Imamul Hassan Bhuiyan, "Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting." Computer Methods and Programs in Biomedicine, Vol. 140pp. 201-10, (2017).
40- Hamidreza Jalali, Majid Pouladian, Ali Moti Nasrabadi, and Azin Movahed, "Sleep stages classification based on feature extraction from music of brain." Heliyon, (2024).
2- Luay Fraiwan, Khaldon Lweesy, Natheer Khasawneh, Mohammad Fraiwan, Heinrich Wenz, and Hartmut Dickhaus, "Classification of sleep stages using multi-wavelet time frequency entropy and LDA." Methods of information in Medicine, Vol. 49 (No. 03), pp. 230-37, (2010).
3- Arnaud Sors, Stéphane Bonnet, Sébastien Mirek, Laurent Vercueil, and Jean-François Payen, "A convolutional neural network for sleep stage scoring from raw single-channel EEG." Biomedical Signal Processing and Control, Vol. 42pp. 107-14, (2018).
4- Vipin Gupta and Ram Bilas Pachori, "FBDM based time-frequency representation for sleep stages classification using EEG signals." Biomedical Signal Processing and Control, Vol. 64p. 102265, (2021).
5- Tehreem Fatima Zaidi and Omar Farooq, "EEG sub-bands based sleep stages classification using Fourier Synchrosqueezed transform features." Expert Systems with Applications, Vol. 212p. 118752, (2023).
6- Dihong Jiang, MA Yu, and WANG Yuanyuan, "Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds." Computer Methods and Programs in Biomedicine, Vol. 178pp. 19-30, (2019).
7- Manish Sharma, Deepanshu Goyal, PV Achuth, and U Rajendra Acharya, "An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank." Computers in biology and medicine, Vol. 98pp. 58-75, (2018).
8- Binish Fatimah, Amit Singhal, and Pushpendra Singh, "A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning." Computers in biology and medicine, Vol. 148p. 105877, (2022).
9- Zuo Huang and Bingo Wing-Kuen Ling, "Sleeping stage classification based on joint quaternion valued singular spectrum analysis and ensemble empirical mode decomposition." Biomedical Signal Processing and Control, Vol. 71p. 103086, (2022).
10- RK Tripathy and U Rajendra Acharya, "Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework." Biocybernetics and Biomedical Engineering, Vol. 38 (No. 4), pp. 890-902, (2018).
11- Raed Mohammed Hussein, Loay E George, and Firas Sabar Miften, "Accurate method for sleep stages classification using discriminated features and single eeg channel." Biomedical Signal Processing and Control, Vol. 84p. 104688, (2023).
12- Yvonne Höller, Aljoscha Thomschewski, Elisabeth Verena Schmid, Peter Höller, Julia Sophia Crone, and Eugen Trinka, "Individual brain-frequency responses to self-selected music." International Journal of Psychophysiology, Vol. 86 (No. 3), pp. 206-13, (2012).
13- Thilo Hinterberger and Gerold Baier, "Parametric orchestral sonification of EEG in real time." IEEE MultiMedia, Vol. 12 (No. 2), pp. 70-79, (2005).
14- ED Adrian, "The bebgee ehythm: potential changes feom the occipital lobes in man." Physiol Lab Camb, Vol. 57 (No. 4), pp. 357-85, (1934).
15- Volker Straebel and Wilm Thoben, "Alvin Lucier's music for solo performer: experimental music beyond sonification." Organised Sound, Vol. 19 (No. 1), pp. 17-29, (2014).
16- Gerold Baier, Thomas Hermann, and Ulrich Stephani, "Event-based sonification of EEG rhythms in real time." Clinical Neurophysiology, Vol. 118 (No. 6), pp. 1377-86, (2007).
17- Sam V Norman-Haignere, Philippe Albouy, Anne Caclin, Josh H McDermott, Nancy G Kanwisher, and Barbara Tillmann, "Pitch-responsive cortical regions in congenital amusia." Journal of Neuroscience, Vol. 36 (No. 10), pp. 2986-94, (2016).
18- Yuan Yuan, Yong-Xiu Lai, Dan Wu, and De-Zhong Yao, "A study on melody tempo with EEG." Journal of Electronic Science and Technology, Vol. 7 (No. 1), pp. 88-91, (2009).
19- Archi Banerjee et al., "Study on brain dynamics by non linear analysis of music induced EEG signals." Physica A: Statistical Mechanics and its Applications, Vol. 444pp. 110-20, (2016).
20- Elvira Brattico, Mari Tervaniemi, Risto Näätänen, and Isabelle Peretz, "Musical scale properties are automatically processed in the human auditory cortex." Brain research, Vol. 1117 (No. 1), pp. 162-74, (2006).
21- Clara E James, Mathias S Oechslin, Christoph M Michel, and Michael De Pretto, "Electrical neuroimaging of music processing reveals mid-latency changes with level of musical expertise." Frontiers in neuroscience, Vol. 11p. 613, (2017).
22- Dan Wu, Chaoyi Li, and Dezhong Yao, "Scale-free brain quartet: artistic filtering of multi-channel brainwave music." PLoS One, Vol. 8 (No. 5), p. e64046, (2013).
23- Jing Lu et al., "Generate the scale-free brain music from BOLD signals." Medicine, Vol. 97 (No. 2), (2018).
24- Carlos M Fernandes, Daria Migotina, and Agostinho C Rosa, "Brain's Night Symphony (BraiNSy): A Methodology for EEG Sonification." IEEE Transactions on Affective Computing, Vol. 12 (No. 1), pp. 103-12, (2018).
25- Carlos M Fernandes, Antonio Mora, Juan Julián Merelo, and Agostinho C Rosa, "Pherogenic Drawings-Generating Colored 2-dimensional Abstract Representations of Sleep EEG with the KANTS Algorithm." in International Conference on Evolutionary Computation Theory and Applications, (2012), Vol. 2: SCITEPRESS, pp. 72-80.
26- Pedro Franco and Alpo Värri, "Experiments of the sonification of the sleep electroencephalogram." Finnish Journal of eHealth and eWelfare, Vol. 7 (No. 2-3), pp. 65-74, (2015).
27- Aurora Tulilaulu, Joonas Paalasmaa, Mikko Waris, and Hannu Toivonen, "Sleep musicalization: Automatic music composition from sleep measurements." in Advances in Intelligent Data Analysis XI: 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings 11, (2012): Springer, pp. 392-403.
28- Richard B Berry et al., "AASM scoring manual updates for 2017 (version 2.4)." Vol. 13, ed: American Academy of Sleep Medicine, (2017), pp. 665-66.
29- Bob Kemp, Aeilko Zwinderman, B Tuk, H Kamphuisen, and J Oberyé, "Sleep-edf database expanded." Physionet org, (2018).
30- Boualem Boashash, "Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals." Proceedings of the IEEE, Vol. 80 (No. 4), pp. 520-38, (1992).
31- Sotiris B Kotsiantis, "Bagging and boosting variants for handling classifications problems: a survey." The Knowledge Engineering Review, Vol. 29 (No. 1), pp. 78-100, (2014).
32- Sanidhya Mangal, Rahul Modak, and Poorva Joshi, "LSTM based music generation system." arXiv preprint arXiv:1908.01080, (2019).
33- Sarthak Agarwal, Vaibhav Saxena, Vaibhav Singal, and Swati Aggarwal, "Lstm based music generation with dataset preprocessing and reconstruction techniques." in 2018 IEEE symposium series on computational intelligence (SSCI), (2018): IEEE, pp. 455-62.
34- Sepp Hochreiter and Jürgen Schmidhuber, "Long short-term memory." Neural computation, Vol. 9 (No. 8), pp. 1735-80, (1997).
35- João Paulo Teixeira, Carla Oliveira, and Carla Lopes, "Vocal acoustic analysis–jitter, shimmer and hnr parameters." Procedia Technology, Vol. 9pp. 1112-22, (2013).
36- Yoav Goldberg, Neural network methods for natural language processing. Springer Nature, (2022).
37- Albert Vilamala, Kristoffer H Madsen, and Lars K Hansen, "Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring." in 2017 IEEE 27th international workshop on machine learning for signal processing (MLSP), (2017): IEEE, pp. 1-6.
38- Nicola Michielli, U Rajendra Acharya, and Filippo Molinari, "Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals." Computers in biology and medicine, Vol. 106pp. 71-81, (2019).
39- Ahnaf Rashik Hassan and Mohammed Imamul Hassan Bhuiyan, "Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting." Computer Methods and Programs in Biomedicine, Vol. 140pp. 201-10, (2017).
40- Hamidreza Jalali, Majid Pouladian, Ali Moti Nasrabadi, and Azin Movahed, "Sleep stages classification based on feature extraction from music of brain." Heliyon, (2024).
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How to Cite
1.
Jalali H, pouladian majid, Motie Nasrabadi A, Movahed A. Sleep Stages Classification Using Music Made From EEG. Frontiers Biomed Technol. 2026;.
