Abstract
The revolution in technology affects many fields and among them the Healthcare system. The application-based computer was developed to help specialists to detect diseases, and to perform some basics operations. In this paper, focus is given on the proposed attempts to detect Epilepsy Disease (ED). Several Computer-Aided Diagnosis (CAD) methods were used to provide the brain’s disease status according to signals related to brain activities. These applications achieved acceptable results but still have their limitations. An intelligence CAD based on the Balanced Communication-Avoiding Support Vector Machine (BCA-SVM) is proposed to detect ED using Electroencephalogram (EEG) signals. This attempt is implemented on a Raspberry Pi 4 as a real board to ensure real-time processing. The CAD-based on BCA-SVM achieved an accuracy of 99.8% and the execution time was around 3.2s satisfying the real-time requirement.
Highlights
Brain disorders such as Alzheimer’s and Epilepsy Disease (ED) [1] are consuming vast resources of the health care system
This paper highlights the main issues of these methods, taking into account the proposed attempts to detect brain diseases and the fact that several methods based on Computer-Aided Diagnosis (CAD) were used to identify ED using EEG signals
The proposed CAD based on the Balanced Communication-Avoiding Support Vector Machine (BCA-SVM) method and using the dataset in [33] is verified and the implementation phase is described
Summary
Brain disorders such as Alzheimer’s and Epilepsy Disease (ED) [1] are consuming vast resources of the health care system. Several methods have been proposed for the automated diagnosis of brain diseases. The EEG is capable to read the brain’s electrical signals generated by neurons measured through the scalp. A Brain-Computer Interface (BCI) system enables individuals to communicate through external devices by using their brain's electrical signals in the recording positions of the scalp. This can help to recognize anomalies of frequency patterns or accomplishing several tasks and seems to have many potential applications. This paper highlights the main issues of these methods, taking into account the proposed attempts to detect brain diseases and the fact that several methods based on CAD were used to identify ED using EEG signals. This paper contributes by highlighting the deficiency of a huge EEG database and techniques that were used may need a hardware accelerator to reduce the time duration of their applications
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