A deep learning approach using a stacked autoencoder neural network architecture effectively compressed EEG data in mHealth systems while preserving total energy consumption.
A stacked autoencoder neural network provides an efficient method for EEG data compression in mHealth systems while preserving energy consumption.
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.
Said et al. (Thu,) conducted a other in EEG data compression. Stacked autoencoder neural network architecture was evaluated on EEG data compression and energy consumption. A deep learning approach using a stacked autoencoder neural network architecture effectively compressed EEG data in mHealth systems while preserving total energy consumption.
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