A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network

A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network Publisher Pubmed

Moradi F^{1, 2, 3} ; Mohammadi H^{1, 3, 4} ; Rezaei M^{1, 5} ; Sariaslani P^{3, 4} ; Razazian N^{3, 4} ; Khazaie H¹ ; Adeli H⁶

Show Affiliations

1. Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
2. Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran
3. Department of Neurology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
4. Clinical Research Development Center, Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran
5. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
6. Department of Biomedical Informatics, Department of Neuroscience, Ohio State University, Columbus, OH, United States

Source: European Neurology Published:2020

Abstract

Introduction: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of sleep-EEG signals using wavelet transform and recurrent neural network (RNN). Methods: Two RNNs were designed and trained separately based on a database of classical guitar pieces and Kurdish tanbur Makams using a long short-term memory model. Moreover, discrete wavelet transform and wavelet packet decomposition were used to determine the association between the EEG signals and musical pitches. Continuous wavelet transform was applied to extract musical beat-based features from the EEG. Then, the pretrained RNN was used to generate music. To test the proposed model, 11 sleep EEGs were mapped onto the guitar and tanbur frequency intervals and presented to the pretrained RNN. Next, the generated music was randomly presented to 2 neurologists. Results: The proposed model classified the sleep stages with an accuracy of >81% for tanbur and more than 93% for guitar musical pieces. The inter-rater reliability measured by Cohen's kappa coefficient (κ) revealed good reliability for both tanbur (κ = 0.64, p < 0.001) and guitar musical pieces (κ = 0.85, p < 0.001). Conclusion: The present EEG sonification method leads to valid sleep staging by clinicians. The method could be used on various EEG databases for classification, differentiation, diagnosis, and treatment purposes. Real-time EEG sonification can be used as a feedback tool for replanning of neurophysiological functions for the management of many neurological and psychiatric disorders in the future. © 2020 S. Karger AG, Basel. Copyright: All rights reserved.

2. Analysis of Eeg Brain Connectivity of Children With Adhd Using Graph Theory and Directional Information Transfer, Biomedizinische Technik (2023)

3. Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification, International Journal of Neural Systems (2022)

Experts (# of related papers)

Bahador Makkiabadi (1)

Style	Citing Format
MLA	Moradi F, et al.. "A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network." European Neurology, vol. 83, no. 5, 2020, pp. 468-486.
APA	Moradi F, Mohammadi H, Rezaei M, Sariaslani P, Razazian N, Khazaie H, Adeli H (2020). A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network. European Neurology, 83(5), 468-486.
Chicago	Moradi F, Mohammadi H, Rezaei M, Sariaslani P, Razazian N, Khazaie H, Adeli H. "A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network." European Neurology 83, no. 5 (2020): 468-486.
Harvard	Moradi F et al. (2020) 'A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network', European Neurology, 83(5), pp. 468-486.
Vancouver	Moradi F, Mohammadi H, Rezaei M, Sariaslani P, Razazian N, Khazaie H, et al.. A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network. European Neurology. 2020;83(5):468-486.
BibTex	@article{ author = {Moradi F and Mohammadi H and Rezaei M and Sariaslani P and Razazian N and Khazaie H and Adeli H}, title = {A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network}, journal = {European Neurology}, volume = {83}, number = {5}, pages = {468-486}, year = {2020} }
RIS	TY - JOUR AU - Moradi F AU - Mohammadi H AU - Rezaei M AU - Sariaslani P AU - Razazian N AU - Khazaie H AU - Adeli H TI - A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network JO - European Neurology VL - 83 IS - 5 SP - 468 EP - 486 PY - 2020 ER -

Science Communicator Platform

Authors

Authors Affiliations

Abstract