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Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages

Publication ,  Conference
Prabhudesai, KS; Collins, LM; Mainsah, BO
Published in: International IEEE/EMBS Conference on Neural Engineering, NER
May 16, 2019

Deep neural networks have emerged as popular machine learning tools due to their ability to automatically learn feature representations from raw input data. An auto-encoder neural network is a special network that can be trained in an unsupervised manner for automated feature learning. Unsupervised analysis of EEG signals is highly desirable since supervised analysis requires manual labeling of EEG signals which can be labor intensive and time consuming given the large amount of EEG data collected. We present a deep convolutional auto-encoder neural network to automatically learn feature representations from raw EEG signals in an unsupervised manner. We use the features extracted from the auto-encoder neural network for clustering EEG signals into sleep stages. For clustering, we test two algorithms: K-means - which is a single-membership model, and the latent Dirichlet allocation (LDA) topic model - which is a mixed membership model. Results are presented demonstrating an improvement in clustering performance using auto-encoder features compared to standard manually extracted features.

Duke Scholars

Published In

International IEEE/EMBS Conference on Neural Engineering, NER

DOI

EISSN

1948-3554

ISSN

1948-3546

Publication Date

May 16, 2019

Volume

2019-March

Start / End Page

937 / 940
 

Citation

APA
Chicago
ICMJE
MLA
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Prabhudesai, K. S., Collins, L. M., & Mainsah, B. O. (2019). Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages. In International IEEE/EMBS Conference on Neural Engineering, NER (Vol. 2019-March, pp. 937–940). https://doi.org/10.1109/NER.2019.8716996
Prabhudesai, K. S., L. M. Collins, and B. O. Mainsah. “Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages.” In International IEEE/EMBS Conference on Neural Engineering, NER, 2019-March:937–40, 2019. https://doi.org/10.1109/NER.2019.8716996.
Prabhudesai KS, Collins LM, Mainsah BO. Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages. In: International IEEE/EMBS Conference on Neural Engineering, NER. 2019. p. 937–40.
Prabhudesai, K. S., et al. “Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages.” International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2019-March, 2019, pp. 937–40. Scopus, doi:10.1109/NER.2019.8716996.
Prabhudesai KS, Collins LM, Mainsah BO. Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages. International IEEE/EMBS Conference on Neural Engineering, NER. 2019. p. 937–940.

Published In

International IEEE/EMBS Conference on Neural Engineering, NER

DOI

EISSN

1948-3554

ISSN

1948-3546

Publication Date

May 16, 2019

Volume

2019-March

Start / End Page

937 / 940