Proposed Title :
FPGA Implementation of Area Optimized and High Speed Seizure Detection System using ELM Classification Method
Improvement of this Project:
To design Epileptic Seizure Detection using ELM Classifier with MCM multiplication and Truncated Multiplier and proved the comparisons of area, delay and power.
Using Epileptologie Seizure Data Sets and tested each set (A-E) using text to binary conversion method.
- Xilinx 14.5
The Electroencephalography (EEG) signal are widely used to determine the brain disorders. These EEG signals are used to record the electrical activity of the human brain and these epileptic seizure is characterized by abnormal electrical activity in the human brain. Once this epileptic seizures occur at inconvenient moments in seizure patients, its causes unexpected death. By analyzing these EEG data signals its allows for the detection and prediction of epileptic seizures and its challenging task to analysis accurate prediction. For that with help of hardware and real time FPGA implementation detection to predict more accurate epileptic seizure detection. In this method a band pass finite impulse response with analyzed a separate range of delta (0-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz) and gamma (>30Hz) brain rhythms. This proposed work presents EEG signal and classification of normal or seizure signal is done by using Extreme Learning Machine (ELM) classifier and area and power delay reduction of this proposed work integrated a Truncated multiplier instead of MCM multiplication in all band of FIR filters and Feature extraction method and ELM Classification. Finally this proposed work designed in Verilog HDL and synthesized in Xilinx Vertex-5 FPGA, and compared all the parameters in terms of area, delay and power.
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FPGA Implementation of Epileptic Seizure Detection Using ELM Classifier
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