Abstract
Nowadays, the mobile healthcare industry is prospering due to the increase in computer processing power, improvement of next-generation communication technologies, and high storage capacity. Mobile multimedia sensors can acquire healthcare data, which can be processed to make decisions on the health status of users. In line with this, we propose a mobile multimedia healthcare framework in this paper, where an automatic seizure detection system is embedded as a case study. In the proposed system, electroencephalogram signals from a head-mounted set are recorded and processed using convolutional neural networks. A classification module determines whether the signals exhibit seizure. Experimental results show that the proposed system can achieve high levels of accuracy and sensitivity. The Children’s Hospital Boston–Massachusetts Institute of Technology database indicates the system accuracy and sensitivity to be 99.02% and 92.35% in a cross-patient scenario, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
