Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2859267 | IEEE ACCESS |
Keywords | Field | DocType |
Mobile multimedia healthcare,seizure detection,convolutional neural network,SVM,EEG signals | Seizure detection,Computer science,Convolutional neural network,Computer network,Feature extraction,Real-time computing,Healthcare industry,Multimedia framework,Computer processing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ghulam Muhammad | 1 | 770 | 60.81 |
| Mehedi Masud | 2 | 77 | 26.95 |
| Syed Umar Amin | 3 | 66 | 7.13 |
| Roobaea Alroobaea | 4 | 16 | 7.70 |
| Mohammed F. Alhamid | 5 | 164 | 21.55 |