@inproceedings{7df787cbbeaf4fa7b226a311291228cc,
title = "Transfer Learning of Spectrogram Image for Automatic Sleep Stage Classification",
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.",
keywords = "Convolutional Neural Network, Deep learning, Discrete Wavelet Transform, Sleep stage classification, Spectrogram, Transfer learning",
author = "Gharbali, {Ali Abdollahi} and Shirin Najdi and Fonseca, {Jos{\'e} Manuel}",
note = "info:eu-repo/grantAgreement/FCT/5876/147324/PT# ; 15th International Conference on Image Analysis and Recognition, ICIAR 2018 ; Conference date: 27-06-2018 Through 29-06-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-93000-8_59",
language = "English",
isbn = "978-3-319-92999-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "522--528",
editor = "{ter Haar Romeny}, B. and F. Karray and A. Campilho",
booktitle = "Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings",
address = "Germany",
}