Name
Abstract | ||
|---|---|---|
Epilepsy, simply put, is an abnormality in the central nervous system that leads to unplanned-for seizures affecting millions of people worldwide. Medication is the most common treatment for all those suffering from epilepsy, however, this paper introduces the idea of designing an implantable/embedded chip that is to be fed with a machine learning algorithm, specifically Support Vector Machine (SVM) to predict seizure periods prior to their occurrence to be able to notify the patient or suppress the seizure from happening. Since the system's problem is binary classification between pre-ictal and normal periods, determining the right set of features along with the SVM kernel function is the first step in designing the chip. This paper proposes two tracks, a Linear Features-Linear Kernel combination with a set of time-domain hardware-inexpensive features namely; Coastline, Absolute Mean, Root Mean Square, and Standard Deviation. In addition to the Non-Linear Feature-RBF Kernel combination with another time-domain feature namely; Hurst Exponent. This paper also introduces the concept of segmenting the testing data which showed extremely promising results in the Non-Linear Feature-RBF Kernel combination. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICM48031.2019.9021703 | 2019 31st International Conference on Microelectronics (ICM) |
Keywords | DocType | ISSN |
Epilepsy,Seizure Prediction,SVM,SMO,Implantable Chip,EEG Signals,Pre-ictal Periods | Conference | 2159-1679 |
ISBN | Citations | PageRank |
978-1-7281-4059-9 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ingy Alaa | 1 | 0 | 0.34 |
| Nourhan Ahmed | 2 | 0 | 0.34 |
| Mostafa Nader | 3 | 0 | 0.34 |
| Abdelrahman Magdy | 4 | 0 | 0.34 |
| Mohamed O. Ibrahim | 5 | 3 | 6.82 |
| Michael Khalil | 6 | 0 | 0.34 |
| Michael H. Zakhari | 7 | 0 | 0.34 |
| Hassan Mostafa | 8 | 1 | 1.37 |