DOI: 10.17706/IJBBB.2020.10.4.154-160
Applying CLDNN to Time-Frequency Image of EEG Signals to Predict Depth of Anesthesia
Index Terms—Electroencephalogram (EEG), continuous wavelet transform (CWT), fully connected deep neural networks (CLDNN), depth of anesthesia (DOA).
Yen-Lin Chen and Jiann-Shing Shieh are with Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan. Shou-Zen Fan is with Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan. Maysam F. Abbod is with Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK.
Cite:Yen-Lin Chen, Shou-Zen Fan, Maysam F. Abbod, Jiann-Shing Shieh, "Applying CLDNN to Time-Frequency Image of EEG Signals to Predict Depth of Anesthesia," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 10, no. 4, pp. 154-160, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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