Abstract
Most of the existing methods for automatic sleep stage classification are relying on hand-crafted features. In this paper, the goal is to develop a deep learning-based method that automatically exploits time-frequency spectrum of Electroencephalogram (EEG) signal, removing the need for manual feature extraction. Using Continuous Wavelet Transform (CWT), we extracted the time-frequency spectrogram for EEG signal of 10 healthy subjects and converted to RGB images. The images were classified using transfer learning of a pre-trained Convolutional Neural Network (CNN), AlexNet. The proposed method was evaluated using a publicly available dataset. Evaluation results show that our method can achieve state of the art accuracy, while having higher overall sensitivity.
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Acknowledgment
Authors would like to thank professor André Damas Mora for the useful comments and suggestions on sleep image processing. This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS.
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Gharbali, A.A., Najdi, S., Fonseca, J.M. (2018). Transfer Learning of Spectrogram Image for Automatic Sleep Stage Classification. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_59
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DOI: https://doi.org/10.1007/978-3-319-93000-8_59
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