Deep learning approach for EEG compression in mHealth system
| Author | Ben Said, Ahmed |
| Author | Mohamed, Amr |
| Author | Elfouly, Tarek |
| Available date | 2021-01-25T06:45:46Z |
| Publication Date | 2017 |
| Publication Name | 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017 |
| Resource | Scopus |
| Abstract | The emergence of mobile health (mHealth) systems has risen the challenges and concerns due to the sensitivity of the data involved in such systems. It is essential to ensure that these data are well delivered to the health monitoring center for accurate and perfect diagnosis and follow-up. Due to the wireless network constraints, these requirements become more challenging. In this paper, we propose a deep learning approach for EEG data compression in mHealth system. We show that the stacked autoencoder neural network architecture is efficient for EEG data compression. We conduct a comprehensive comparative study that demonstrates the effectiveness of our system for EEG compression in addition to preserving the total energy consumption. |
| Sponsor | This publication was made possible by NPRP grant #7-684-1-127 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
| Language | en |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Subject | Compression EEG MHealth Stacked autoencoder |
| Type | Conference Paper |
| Pagination | 1508-1512 |
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