In the feature extraction method discrete wavelet transform will level Daubechies to fit the physiological bands of electroencephalogram (EEG) signals. In this work where design a high efficiency and attain high detection accuracy system of seizure detection in system on chip, this paper present with non linear support vector machine (SVM) with classifications method and also consists of seizure prediction feature extraction method. In this recent research of automatic seizure detection system is very convenient to monitor epilepsy patients in anywhere at the time of predictions previously and alert hospitals and related persons. To develop SVM based Seizure Detection System with using Truncation Multiplier, and hence proved the Area, Power and Delay with using Synthesized in Xilinx XC6SLX100T-2CSG484 FPGA.Proposed Title : FPGA Implementation of SVM based Seizure Detection System with using Truncation Multiplier List of the following materials will be included with the Downloaded Backup: Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The FE module performs the three level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time–frequency domain features reflecting the non stationary signal properties. The proposed design mainly consists of a feature extraction (FE) module and an SVM module. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). Portable automatic seizure detection system is very convenient for epilepsy patients to carry.

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