| Idea Name: |
Epilepto-Pace |
| Slogan: |
Efficient seizure detection on chip |
| Supervisor Name: |
Dr. Faisal Shafait |
| Supervisor Designation: |
Professor and Researcher |
| Supervisor School: |
NUST-SEECS |
| Supervisor Department: |
Department of Computing |
| Contact number: |
nill |
| Email ID: |
faisal.shafait@seecs.edu.pk |
| Abstract: |
| The main purpose of this project is paced-up detection of the patients that are suffering with epilepsy and to classify between the normal and the healthy patients using ANN based FPGA architecture |
| What is the unmet need in society that your idea will fulfill ? |
| 2. Epilepsy detection devices available in the market but we are looking to implement the detection system on vanguard devices such as FPGA where the energy cost is too high. We will be developing the most robust system that is high efficiency and accuracy. |
| Who needs it ? How many would benefit ? |
| 3. Our prototype is a biomedical product. It would be used by the doctors in the hospitals or clinics for the quick detection of patients suffering with epileptic seizure attacks. Our device can be used as a prototype for the research purposes for classification models implementation on hardware. |
| How will the solution works |
| Our solution is designed in such a way that particular EEG data is feature extracted and trained. The model is fed in the FPGA and Deep recursive neural networks are used for efficient resource utilization and minimum power consumption |
| Who are your competitors ? How is your solution different |
| Most of the solutions available are based on ASIC (Application Specific Integrated Circuit) or SoC (System on chip). This type of high hardware has high recurring replacement costs due to no re-programmability feature. In contrast, our solution is highly flexible in terms of hardware performance. |
| Status: |
new |
| Entry Date & Time: |
2020-12-20 (1859) |
