• Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems 

      Al-Marridi A.Z.; Mohamed A.; Erbad A. ( Institute of Electrical and Electronics Engineers Inc. , 2018 , Conference Paper)
      In the last few years, the number of patients with chronic diseases requiring constant monitoring increased rapidly, which motivates researchers to develop scalable remote health applications. Nevertheless, the amount of ...
    • Deep learning approach for EEG compression in mHealth system 

      Ben Said, Ahmed; Mohamed, Amr; Elfouly, Tarek ( Institute of Electrical and Electronics Engineers Inc. , 2017 , Conference Paper)
      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 ...
    • Efficient EEG mobile edge computing and optimal resource allocation for smart health applications 

      Al-Marridi, Abeer Z.; Mohamed, Amr; Erbad, Aiman; Al-Ali, Abdulla; Guizani, Mohsen ( Institute of Electrical and Electronics Engineers Inc. , 2019 , Conference Paper)
      In the past few years, a rapid increase in the number of patients requiring constant monitoring, which inspires researchers to develop intelligent and sustainable remote smart healthcare services. However, the transmission ...
    • Wavelet-based Encoding Scheme for Controlling Size of Compressed ECG Segments in Telecardiology Systems 

      Al-Busaidi, Asiya M.; Khriji, Lazhar; Touati, Farid; Rasid, Mohd Fadlee; Mnaouer, Adel Ben ( Springer New York LLC , 2017 , Article)
      One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets ...